Climate variability and climate change influence human migration both directly and indirectly through a variety of channels that are controlled by individual and household socioeconomic, cultural, and psychological processes as well as public policies and network effects. Characterizing and predicting migration flows are thus extremely complex and challenging. Among the quantitative methods available for predicting such flows is the widely used gravity model that ignores the network autocorrelation among flows and thus may lead to biased estimation of the climate effects of interest. In this study, we use a network model, the additive and multiplicative effects model for network (AMEN), to investigate the effects of climate variability, migrant networks, and their interactions on South African internal migration. Our results indicate that prior migrant networks have a significant influence on migration and can modify the association between climate variability and migration flows. We also reveal an otherwise obscure difference in responses to these effects between migrants moving to urban and non-urban destinations. With different metrics, we discover diverse drought effects on these migrants; for example, the negative standardized precipitation index (SPI) with a timescale of 12 months affects the non-urban-oriented migrants’ destination choices more than the rainy season rainfall deficit or soil moisture do. Moreover, we find that socioeconomic factors such as the unemployment rate are more significant to urban-oriented migrants, while some unobserved factors, possibly including the abolition of apartheid policies, appear to be more important to non-urban-oriented migrants.
Future coastal flood hazard at many locations will be impacted by both tropical cyclone (TC) change and relative sea-level rise (SLR). Despite sea level and TC activity being influenced by common thermodynamic and dynamic climate variables, their future changes are generally considered independently. Here, we investigate correlations between SLR and TC change derived from simulations of 26 Coupled Model Intercomparison Project Phase 6 models. We first explore correlations between SLR and TC activity by inference from two large-scale factors known to modulate TC activity: potential intensity (PI) and vertical wind shear. Under the high emissions SSP5-8.5, SLR is strongly correlated with PI change (positively) and vertical wind shear change (negatively) over much of the western North Atlantic and North West Pacific, with global mean surface air temperature (GSAT) modulating the co-variability. To explore the impact of the joint changes on flood hazard, we conduct climatological–hydrodynamic modeling at five sites along the US East and Gulf Coasts. Positive correlations between SLR and TC change alter flood hazard projections, particularly at Wilmington, Charleston and New Orleans. For example, if positive correlations between SLR and TC changes are ignored in estimating flood hazard at Wilmington, the average projected change to the historical 100 years storm tide event is under-estimated by 12%. Our results suggest that flood hazard assessments that neglect the joint influence of these factors and that do not reflect the full distribution of GSAT change may not accurately represent future flood hazard.
Estimates of changes in the frequency or height of contemporary extreme sea levels (ESLs) under various climate change scenarios are often used by climate and sea level scientists to help communicate the physical basis for societal concern regarding sea level rise. Changes in ESLs (i.e., the hazard) are often represented using various metrics and indicators that, when anchored to salient impacts on human systems and the natural environment, provide useful information to policy makers, stakeholders, and the general public. While changes in hazards are often anchored to impacts at local scales, aggregate global summary metrics generally lack the context of local exposure and vulnerability that facilitates translating hazards into impacts. Contextualizing changes in hazards is also needed when communicating the timing of when projected ESL frequencies cross critical thresholds, such as the year in which ESLs higher than the design height benchmark of protective infrastructure (e.g., the 100-year water level) are expected to occur within the lifetime of that infrastructure. We present specific examples demonstrating the need for such contextualization using a simple flood exposure model, local sea level rise projections, and population exposure estimates for 414 global cities. We suggest regional and global climate assessment reports integrate global, regional, and local perspectives on coastal risk to address hazard, vulnerability and exposure simultaneously.
Habitability loss is increasingly recognized as an important dimension of climate risk assessment and one with complex linkages to migration. Most habitability assessments, like climate risk assessments more generally, are based on “top-down” approaches that apply quantitative models using uniform methodologies and generalizable assumptions at global and regional scales, privileging physical sciences over social science–informed understandings of local vulnerability and adaptive capacity. Many assessments have focused on a single climate hazard threshold (such as permanent inundation or the 1-in-100-year flood), and a subset have implied that outmigration may be one of the few viable adaptation responses (1). There is a risk that such climate determinism minimizes the potential for human agency to find creative, locally appropriate solutions. Although top-down modeling can serve a useful purpose in identifying potential future “hot spots” for habitability decline and potential outmigration, only by integrating “bottom-up” insights related to place-based physical systems and social contexts, including potential adaptive responses, will we arrive at a more nuanced understanding. This integrated framework would encourage development of policies that identify the most feasible and actionable local adaptation options across diverse geographies and groups, rather than options that are deterministic and one-size-fits-all and encourage binary “migrate or not” decisions. We propose a set of recommendations centered around building the research and assessment knowledge base most needed to inform policy responses around habitability loss and migration.
Climate change is anticipated to impact smallholder farmer livelihoods substantially. However, empirical evidence is inconclusive regarding how increased climate stress affects smallholder farmers’ deployment of various livelihood strategies, including rural–urban migration. Here we use an agent-based model to show that in a South Asian agricultural community experiencing a 1.5 oC temperature increase by 2050, climate impacts are likely to decrease household income in 2050 by an average of 28%, with fewer households investing in both economic migration and cash crops, relative to a stationary climate. Pairing a small cash transfer with risk transfer mechanisms significantly increases the adoption of migration and cash crops, improves community incomes and reduces community inequality. While specific results depend on contextual factors such as risk preferences and climate risk exposure, these interventions are robust in improving adaptation outcomes and alleviating immobility, by addressing the intersection of risk aversion, financial constraints and climate impacts.
Coastal climate adaptation public works, such as storm surge barriers and levees, are central elements of several current proposals to limit damages from coastal storms and sea-level rise in the United States. Academic analysis of these public works projects is dominated by technocratic and engineering-driven frameworks. However, social conflict, laws, political incentives, governance structures, and other political factors have played pivotal roles in determining the fate of government-led coastal flood risk reduction efforts. Here, we review the ways in which politics has enabled or hindered the conception, design, and implementation of coastal risk reduction projects in the U.S. We draw from the literature in natural hazards, infrastructure, political science, and climate adaptation and give supporting examples. Overall, we find that (1) multiple floods are often needed to elicit earnest planning; (2) strong and continuous leadership from elected officials is necessary to advance projects; (3) stakeholder participation during the design stage has improved outcomes; (4) legal challenges to procedural and substantive shortcomings under environmental protection statutes present an enduring obstacle to implementing megastructure proposals.
Sea-level rise and ensuing permanent coastal inundation will cause spatial shifts in population and economic activity over the next 200 years. Using a highly spatially disaggregated, dynamic model of the world economy that accounts for the dynamics of migration, trade, and innovation, this paper estimates the consequences of probabilistic projections of local sea-level changes under diﬀerent emissions scenarios. Under an intermediate greenhouse gas concentration trajectory, permanent ﬂooding is projected to reduce global real GDP by an average of 0.19% in present value terms, with welfare declining by 0.24% as people move to places with less attractive amenities. By the year 2200 a projected 1.46% of world population will be displaced. Losses in many coastal localities are more than an order of magnitude larger, with some low-lying urban areas particularly hard hit. When ignoring the dynamic economic adaptation of investment and migration to ﬂooding, the loss in real GDP in 2200 increases from 0.11% to 4.5%. This shows the importance of including dynamic adaptation in future loss models.
Migration may be increasingly used as adaptation strategy to reduce populations’ exposure and vulnerability to climate change impacts. Conversely, either through lack of information about risks at destinations or as outcome of balancing those risks, people might move to locations where they are more exposed to climatic risk than at their origin locations. Climate damages, whose quantification informs understanding of societal exposure and vulnerability, are typically computed by integrated assessment models (IAMs). Yet migration is hardly included in commonly used IAMs. In this paper, we investigate how border policy, a key influence on international migration flows, affects exposure and vulnerability to climate change impacts. To this aim, we include international migration and remittance dynamics explicitly in a widely used IAM employing a gravity model and compare four scenarios of border policy. We then quantify effects of border policy on population distribution, income, exposure, and vulnerability and of CO2 emissions and temperature increase for the period 2015 to 2100 along five scenarios of future development and climate change. We find that most migrants tend to move to areas where they are less exposed and vulnerable than where they came from. Our results confirm that migration and remittances can positively contribute to climate change adaptation. Crucially, our findings imply that restrictive border policy can increase exposure and vulnerability, by trapping people in areas where they are more exposed and vulnerable than where they would otherwise migrate. These results suggest that the consequences of migration policy should play a greater part in deliberations about international climate policy.
Uncertainties in Representative Concentration Pathway (RCP) scenarios and Antarctic Ice Sheet (AIS) melt propagate into uncertainties in projected mean sea-level (MSL) changes and extreme sea-level (ESL) events. Here we quantify the impact of RCP scenarios and AIS contributions on 21st-century ESL changes at tide-gauge sites across the globe using extreme-value statistics. We find that even under RCP2.6, almost half of the sites could be exposed annually to a present-day 100-year ESL event by 2050. Most tropical sites face large increases in ESL events earlier and for scenarios with smaller MSL changes than extratropical sites. Strong emission reductions lower the probability of large ESL changes but due to AIS uncertainties, cannot fully eliminate the probability that large increases in frequencies of ESL events will occur. Under RCP8.5 and rapid AIS mass loss, many tropical sites, including low-lying islands face a MSL rise by 2100 that exceeds the present-day 100-year event level.
Extreme weather and climate events and their impacts can occur in complex combinations, an interaction shaped by physical drivers and societal forces. In these situations, governance, markets and other decision-making structures—together with population exposure and vulnerability—create nonphysical interconnections among events by linking their impacts, to positive or negative effect. Various anthropogenic actions can also directly affect the severity of events, further complicating these feedback loops. Such relationships are rarely characterized or considered in physical-sciences-based research contexts. Here, we present a multidisciplinary argument for the concept of connected extreme events, and we suggest vantage points and approaches for producing climate information useful in guiding decisions about them.
Minimizing the adverse consequences of sea-level change presents a key societal challenge. New modelling is necessary to examine the implications of global policy decisions that determine future greenhouse gas emissions and local policies around coastal risk that influence where and how we live.
Agent-based modeling (ABM) has transformed the century-old field of mechanistic migration modeling, by shifting the unit of analysis from the city (in the gravity model) to the individual decision maker. Various efforts over the past decade have leveraged ABM tools to integrate competing labor opportunities, climatic shocks, and sharing across networks into decision-based models of migration patterns. We present the MIDAS (Migration, Intensification, and Diversification as Adaptive Strategies) framework, which draws on the ‘push-pull-mooring’ (PPM) theory of migration to integrate the influences of social networks, climatic shifts, and opportunities for livelihoods diversification on migration in a single framework. We demonstrate some of the strategic responses to opportunities that are possible in a true PPM modeling framework, including substitution of income streams, the choice to specialize or diversify, as well as to migrate in response to shocks. We observe what may be the emergence of a distinct class of agents within one of our experiments, highlighting the value of tools like MIDAS to capture migration and adaptive behaviors under conditions for which analogs do not yet exist in census datasets or otherwise. Importantly, we show how adaptation decisions depend strongly on a small number of behavioral parameters, key among them preferences for risk, for different forms of utility, and for time.
Despite considerable advances in process understanding, numerical modeling, and the observational record of ice sheet contributions to global mean sea-level rise (SLR) since the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change, severe limitations remain in the predictive capability of ice sheet models. As a consequence, the potential contributions of ice sheets remain the largest source of uncertainty in projecting future SLR. Here, we report the findings of a structured expert judgement study, using unique techniques for modeling correlations between inter- and intra-ice sheet processes and their tail dependences. We find that since the AR5, expert uncertainty has grown, in particular because of uncertain ice dynamic effects. For a +2 °C temperature scenario consistent with the Paris Agreement, we obtain a median estimate of a 26 cm SLR contribution by 2100, with a 95th percentile value of 81 cm. For a +5 °C temperature scenario more consistent with unchecked emissions growth, the corresponding values are 51 and 178 cm, respectively. Inclusion of thermal expansion and glacier contributions results in a global total SLR estimate that exceeds 2 m at the 95th percentile. Our findings support the use of scenarios of 21st century global total SLR exceeding 2 m for planning purposes. Beyond 2100, uncertainty and projected SLR increase rapidly. The 95th percentile ice sheet contribution by 2200, for the +5 °C scenario, is 7.5 m as a result of instabilities coming into play in both West and East Antarctica. Introducing process correlations and tail dependences increases estimates by roughly 15%.
Global mean sea-level rise (SLR), which during the last quarter century has occurred at an accelerating rate (1), averaging about +3 mm⋅y−1, threatens coastal communities and ecosystems worldwide. Adaptation measures accounting for the changing hazard, including building or raising permanent or movable structures such as surge barriers and sea walls, enhancing nature-based defenses such as wetlands, and selective retreat of populations and facilities from areas threatened by episodic flooding or permanent inundation, are being planned or implemented in several countries. Risk assessment for such adaptation efforts requires projections of future SLR, including careful characterization and evaluation of uncertainties (2) and regional projections that account for ocean dynamics, gravitational and rotational effects, and vertical land motion (3). During the nearly 40 y since the first modern, scientific assessments of SLR, understanding of the various causes of this rise has advanced substantially. Improvements during the past decade include closing the historic sea-level budget, attributing global mean SLR to human activities, confirming acceleration of SLR since the nineteenth century and during the satellite altimetry era, and developing analytical frameworks for estimating regional and local mean sea level and extreme water level changes. Nonetheless, long-term SLR projections remain acutely uncertain, in large part because of inadequate understanding of the potential future behaviors of the Greenland and Antarctic ice sheets and their responses to future global climate change. This limitation is especially troubling, given that the ice sheet influence on SLR has been increasing since the 1990s (4) and has overtaken mountain glaciers to become the largest barystatic (mass) contribution to SLR (5). In addition, for any given future climate scenario, the ice sheets constitute the component with the largest uncertainties by a substantial margin, especially beyond 2050 (6).
Advances since the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (7) include improved process understanding and representation in deterministic ice sheet models (8, 9), probabilistic projections calibrated against these models and the observational record (10), and new semiempirical models, based on the historical relationship between temperature and sea-level changes. Each of these approaches has limitations that stem from factors including poorly understood processes, poorly constrained boundary conditions, and a short (∼25 y) satellite observation record of ice sheets that does not capture the time scales of internal variability in the ice sheet climate system. As a consequence, it is unclear to what extent recent observed ice sheet changes (11) are a result of internal variability (ice sheet weather) or external forcing (ice sheet climate).
Where other methods are intractable for scientific or practical reasons, structured expert judgement (SEJ), using calibrated expert responses, provides a formal approach for estimating uncertain quantities according to current scientific understanding. It has been used in a wide range of applications, including natural and anthropogenic hazards such as earthquakes, volcanic eruptions, vector-borne disease spread, and nuclear waste security (12). That said, it should not be regarded as a substitute for fundamental research into driving processes, but instead as a source of complementary insights into the current state of knowledge and, in particular, the extent of the uncertainties (12). An SEJ study conducted in advance of the AR5 (13) (hereafter BA13) provided valuable insights into the uncertainties around ice sheet projections, as assessed at that time.
Since then, regional- and continental-scale, process-based modeling of ice sheets has advanced substantially (8, 9, 14–16), with the inclusion of new positive feedbacks that could potentially accelerate mass loss, and negative feedbacks that could potentially slow it. These include solid Earth and gravitational processes (17, 18), Antarctic marine ice cliff instability (19), and the influences of organic and inorganic impurities on the albedo of the Greenland Ice Sheet (20). The importance of these feedbacks is an area of continuing research. Therefore, alternative approaches must be exploited to assess future SLR and, critically, its associated uncertainties (21), to serve the more immediate needs of the science and policy communities.
Here, we report the findings of an SEJ exercise undertaken in 2018 via separate, 2-d workshops held in the United States and United Kingdom, involving 22 experts (hereafter SEJ2018). Details of how experts were selected are provided in SI Appendix, Note 1. The questions and format of the workshops were identical, so that the findings could be combined using an impartial weighting approach (Methods). SEJ (as opposed to other types of expert elicitation) weights each expert using objective estimates of their statistical accuracy and informativeness (22), determined using experts’ uncertainty evaluations over a set of seed questions from their field with ascertainable values (Methods). The approach is analogous to weighting climate models based on their skill in capturing a relevant property, such as the regional 20th century surface air temperature record (23). In SEJ, the synthetic expert (i.e., the performance weighted [PW] combination of all of the experts’ judgments) in general outperforms an equal weights (EW) combination in terms of statistical accuracy and informativeness, as illustrated in SI Appendix, Fig. S3. The approach is particularly effective at identifying those experts who are able to quantify their uncertainties with high statistical accuracy for specified problems rather than, for example, experts with restricted domains of knowledge or even high scientific reputation (12).
The participating experts quantified their uncertainties for three physical processes relevant to ice sheet mass balance: accumulation, discharge, and surface runoff. They did this for each of the Greenland, West Antarctic, and East Antarctic ice sheets (GrIS, WAIS, and EAIS, respectively), and for two schematic temperature change scenarios. The first temperature trajectory (denoted L) stabilized in 2100 at +2 °C above preindustrial global mean surface air temperature (defined as the average for 1850–1900), and the second (denoted H) stabilized at +5 °C (SI Appendix, Fig. S1). The experts generated values for four dates: 2050, 2100, 2200, and 2300. Experts also quantified the dependence between accumulation, runoff, and discharge within each of the three ice sheets, and between each ice sheet for discharge only, for the H scenario in 2100. We used temperature trajectories rather than emissions scenarios to isolate the experts’ judgements about the relationship between global mean surface air temperature change and ice sheet changes from judgements about climate sensitivity.
An important and unique element of SEJ2018 was the elicitation of intra- and inter-ice sheet dependencies (SI Appendix, Note 1.5). Two features of dependence were elicited: a central dependence and an upper tail dependence. The former captures the probability that one variable exceeds its median given that the other variable exceeds its median, whereas the latter captures the probability that one variable exceeds its 95th percentile given that the other exceeds its 95th percentile. It is well known that these two types of dependence are, in general, markedly different, a property that is not captured by the usual Gaussian dependence model. The latter always imposes tail independence, regardless of the degree of central dependence, and can produce large errors when applied inappropriately (24). For example, if GrIS discharge exceeds its 95th percentile, what is the probability that runoff will also exceed its 95th percentile? This probability may be substantially higher than the independent probability of 5%, and ignoring tail dependence may lead to underestimating the probability of high SLR contributions. On the basis of each expert’s responses, a joint distribution was constructed to capture the dependencies among runoff, accumulation, and discharge for GrIS, WAIS, and EAIS, with dependence structures chosen, per expert, to capture central and tail dependences (Methods and SI Appendix, Note 1.5). In BA13, heuristic dependency values were applied on the basis of simple assumptions about the response of processes to a common forcing.
To help interpret the findings, experts were also asked to provide qualitative and rank-order information on what they regard to be the leading processes that could influence ice dynamics and surface mass balance (snowfall minus ablation); henceforth, this is termed the descriptive rationale. Further details can be found in the SI Appendix. The combined sea-level contribution from all processes and ice sheets was determined assuming either independence or dependence. Here, we focus on the findings with dependence; we examine the effect of the elicited dependencies and the approach taken in SI Appendix, Note 1.5.
The ice sheet contributions were expressed as anomalies from the 2000–2010 mean states, which were predefined (SI Appendix, Table S7). The baseline sea-level contribution for this period was prescribed as 0.76 mm⋅y−1 (0.56, 0.20, and 0.00 mm⋅y−1 for GrIS, WAIS, and EAIS, respectively) and has been added to the elicited values discussed here. This is close to an observationally derived value of 0.79 mm⋅y−1 for the same period, which was published subsequently to the SEJ workshops (4).
Sea level rise amplifies flooding from tides and storms for coastal communities around the globe. Although the characterization of these physical hazards has improved, it is people’s behavior that will ultimately determine the impact on communities. This study adds to our understanding of how people may respond to various adaptation options and policies, using a household survey in New York City, New York, neighborhoods affected by Hurricane Sandy. We investigate previously overlooked factors that may influence intended household adaptive behavior, such as single-action bias, a cognitive trade-off that households make between adaptation options, whereby taking a small (and often less effective measure) may strongly discourage uptake of a more protective measure. Through a novel application of discrete choice experiments in the coastal adaptation context, we simulate plausible future conditions to assess potential adaptation under climatic and nonclimatic stressors. Our findings suggest that single-action bias plays a substantial role in intended coastal adaptation, whereby the odds of homeowners who have already implemented a modest-cost measure to insure and relocate in the future are 66% and 80% lower, respectively. The odds of homeowners to relocate are also ~1.9, ~2.2, and ~3.1 times as great if their peers relocate, nuisance flooding becomes a frequent occurrence, and property values fall substantially, respectively. We find that renters’ motivation to relocate is largely driven more by external issues such as crime, gentrification, and economic security than by flood hazard.
The temporal structure of heat waves having substantial human impact varies widely, with many featuring a compound structure of hot days interspersed with cooler breaks. In contrast, many heat wave definitions employed by meteorologists include a continuous threshold‐exceedance duration criterion. This study examines the hazard of these diverse sequences of extreme heat in the present, and their change with global warming. We define compound heat waves to include those periods with additional hot days following short breaks in heat wave duration. We apply these definitions to analyze daily temperature data from observations, NOAA Geophysical Fluid Dynamics Laboratory global climate model simulations of the past and projected climate, and synthetically generated time series. We demonstrate that compound heat waves will constitute a greater proportion of heat wave hazard as the climate warms and suggest an explanation for this phenomenon. This result implies that in order to limit heat‐related mortality and morbidity with global warming, there is a need to consider added vulnerability caused by the compounding of heat waves.
Heat waves (HWs) are among the most damaging climate extremes to human society. Climate models consistently project that HW frequency, severity, and duration will increase markedly over this century. For urban residents, the urban heat island (UHI) effect further exacerbates the heat stress resulting from HWs. Here we use a climate model to investigate the interactions between the UHI and HWs in 50 cities in the United States under current climate and future warming scenarios. We examine UHI2m (defined as urban-rural difference in 2m-height air temperature) and UHIs (defined as urban-rural difference in radiative surface temperature). Our results show significant sensitivity of the interaction between UHI and HWs to local background climate and warming scenarios. Sensitivity also differs between daytime and nighttime. During daytime, cities in the temperate climate region show significant synergistic effects between UHI and HWs in current climate, with an average of 0.4 K higher UHI2m or 2.8 K higher UHIs during HWs than during normal days. These synergistic effects, however, diminish in future warmer climates. In contrast, the daytime synergistic effects for cities in dry regions are insignificant in the current climate, but emerge in future climates. At night, the synergistic effects are similar across climate regions in the current climate, and are stronger in future climate scenarios. We use a biophysical factorization method to disentangle the mechanisms behind the interactions between UHI and HWs that explain the spatial-temporal patterns of the interactions. Results show that the difference in the increase of urban versus rural evaporation and enhanced anthropogenic heat emissions (air conditioning energy use) during HWs are key contributors to the synergistic effects during daytime. The contrast in water availability between urban and rural land plays an important role in determining the contribution of evaporation. At night, the enhanced release of stored and anthropogenic heat during HWs are the primary contributors to the synergistic eff
The present study examines the lifetime evolution of outer tropical cyclone (TC) size and structure in the North Atlantic (NA) and western North Pacific (WNP). The metric for outer TC size is the radius at which the azimuthal-mean 10-m azimuthal wind equals 8 m s−1 (r8) derived from the NCEP Climate Forecast System Reanalysis (CFSR) and GFDL High-Resolution Forecast-Oriented Low Ocean Resolution model (HiFLOR). Radial profiles of the azimuthal-mean 10-m azimuthal wind are also analyzed to demonstrate that the results are robust across a broad range of wind radii. The analysis shows that most TCs in both basins are characterized by 1) minimum lifetime r8 at genesis, 2) subsequent substantial increases in r8 as the TC wind field expands, 3) peak r8 values occurring near or after the midpoint of the TC lifetime, and 4) nontrivial decreases in r8 and outer winds during the latter part of the TC lifetime. Compared to the NA, WNP TCs are systematically larger up until the end of their lifetime, exhibit r8 growth and decay rates that are larger in magnitude, and are characterized by an earlier onset of lifetime maximum r8 near their lifetime midpoint. In both basins, the TCs exhibiting the largest r8 increases are the longest lived, especially those that traverse the longest distances (i.e., recurving TCs). Finally, analysis of TCs undergoing extratropical transition (ET) shows that NA TCs exhibit negligible changes in r8 during ET, while WNP ET cases either show r8 decreases (CFSR) or negligible changes in r8 (HiFLOR).
Sea-level rise (SLR) is magnifying the frequency and severity of extreme sea levels (ESLs) that can cause coastal flooding. The rate and amount of global mean sea-level (GMSL) rise is a function of the trajectory of global mean surface temperature (GMST). Therefore, temperature stabilization targets (e.g. 1.5 °C and 2.0 °C of warming above pre-industrial levels, as from the Paris Agreement) have important implications for coastal flood risk. Here, we assess, in a global network of tide gauges, the differences in the expected frequencies of ESLs between scenarios that stabilize GMST warming at 1.5 °C, 2.0 °C, and 2.5 °C above pre-industrial levels. We employ probabilistic, localized SLR projections and long-term hourly tide gauge records to estimate the expected frequencies of historical and future ESLs for the 21st and 22nd centuries. By 2100, under 1.5 °C, 2.0 °C, and 2.5 °C GMST stabilization, the median GMSL is projected to rise 48 cm (90% probability of 28–82 cm), 56 cm (28–96 cm), and 58 cm (37–93 cm), respectively. As an independent comparison, a semi-empirical sea level model calibrated to temperature and GMSL over the past two millennia estimates median GMSL rise within 7–8 cm of these projections. By 2150, relative to the 2.0 °C scenario and based on median sea level projections, GMST stabilization of 1.5 °C spares the inundation of lands currently home to about 5 million people, including 60 000 individuals currently residing in Small Island Developing States. We quantify projected changes to the expected frequency of historical 10-, 100-, and 500-year ESL events using frequency amplification factors that incorporate uncertainty in both local SLR and historical return periods of ESLs. By 2150, relative to a 2.0 °C scenario, the reduction in the frequency amplification of the historical 100 year ESL event arising from a 1.5 °C GMST stabilization is greatest in the eastern United States, with ESL event frequency amplification being reduced by about half at most tide gauges. In general, smaller reductions are projected for Small Island Developing States.
Greenland Ice Sheet (GIS) might have lost a large amount of its volume during the last interglacial and may do so again in the future due to climate warming. In this study, we test whether the climate response to the glacial meltwater is sensitive to its discharging location. Two fully coupled atmosphere–ocean general circulation models, CM2G and CM2M, which have completely different ocean components are employed to do the test. In each experiment, a prescribed freshwater flux of 0.1 Sv is discharged from one of the four locations around Greenland—Petermann, 79 North, Jacobshavn and Helheim glaciers. The results from both models show that the AMOC weakens more when the freshwater is discharged from the northern GIS (Petermann and 79 North) than when it is discharged from the southern GIS (Jacobshavn and Helheim), by 15% (CM2G) and 31% (CM2M) averaged over model year 50–300 (CM2G) and 70–300 (CM2M), respectively. This is due to easier access of the freshwater from northern GIS to the deepwater formation site in the Nordic Seas. In the long term (> 300 year), however, the AMOC change is nearly the same for freshwater discharged from any location of the GIS. The East Greenland current accelerates with time and eventually becomes significantly faster when the freshwater is discharged from the north than from the south. Therefore, freshwater from the north is transported efficiently towards the south first and then circulates back to the Nordic Seas, making its impact to the deepwater formation there similar to the freshwater discharged from the south. The results indicate that the details of the location of meltwater discharge matter if the short-term (< 300 years) climate response is concerned, but may not be critical if the long-term (> 300 years) climate response is focused upon.
This study investigated how subsurface and atmospheric leakage from geologic CO2 storage reservoirs could impact the deployment of Carbon Capture and Storage (CCS) in the global energy system. The Leakage Risk Monetization Model was used to estimate the costs of leakage for representative CO2 injection scenarios, and these costs were incorporated into the Global Change Assessment Model. Worst-case scenarios of CO2 leakage risk, which assume that all leakage pathway permeabilities are extremely high, were simulated. Even with this extreme assumption, the associated costs of monitoring, treatment, containment, and remediation resulted in minor shifts in the global energy system. For example, the reduction in CCS deployment in the electricity sector was 3% for the “high” leakage scenario, with replacement coming from fossil fuel and biomass without CCS, nuclear power, and renewable energy. In other words, the impact on CCS deployment under a realistic leakage scenario is likely to be negligible. We also quantified how the resulting shifts will impact atmospheric CO2 concentrations. Under a carbon tax that achieves an atmospheric CO2 concentration of 480 ppm in 2100, technology shifts due to leakage costs would increase this concentration by less than 5 ppm. It is important to emphasize that this increase does not result from leaked CO2 that reaches the land surface, which is minimal due to secondary trapping in geologic strata above the storage reservoir. The overall conclusion is that leakage risks and associated costs will likely not interfere with the effectiveness of policies for climate change mitigation.
Mechanisms such as ice‐shelf hydrofracturing and ice‐cliff collapse may rapidly increase discharge from marine‐based ice sheets. Here, we link a probabilistic framework for sea‐level projections to a small ensemble of Antarctic ice‐sheet (AIS) simulations incorporating these physical processes to explore their influence on global‐mean sea‐level (GMSL) and relative sea‐level (RSL). We compare the new projections to past results using expert assessment and structured expert elicitation about AIS changes. Under high greenhouse gas emissions (Representative Concentration Pathway [RCP] 8.5), median projected 21st century GMSL rise increases from 79 to 146 cm. Without protective measures, revised median RSL projections would by 2100 submerge land currently home to 153 million people, an increase of 44 million. The use of a physical model, rather than simple parameterizations assuming constant acceleration of ice loss, increases forcing sensitivity: overlap between the central 90% of simulations for 2100 for RCP 8.5 (93–243 cm) and RCP 2.6 (26–98 cm) is minimal. By 2300, the gap between median GMSL estimates for RCP 8.5 and RCP 2.6 reaches >10 m, with median RSL projections for RCP 8.5 jeopardizing land now occupied by 950 million people (versus 167 million for RCP 2.6). The minimal correlation between the contribution of AIS to GMSL by 2050 and that in 2100 and beyond implies current sea‐level observations cannot exclude future extreme outcomes. The sensitivity of post‐2050 projections to deeply uncertain physics highlights the need for robust decision and adaptive management frameworks.
The Paris Agreement cemented a new framework for global climate policy based on the voluntary and non-legally binding emission reduction actions by both developed and developing countries. The building blocks strategy for climate action discussed in this Special Issue is well adapted to and strongly complements this new structure. Building blocks focus on multiple transnational mechanisms for mobilizing a wide range of both public and private actors to take actions that reduce emissions by capturing incentives other than climate mitigation as such. The initial commitments by countries under the Paris Agreement are insufficient to meet the level of action required to stabilize the global climate system at a safe level. As such, new voluntary action by public and private actors will be required. The building blocks strategy, and the examples presented in this Special Issue, offers answers to the question of how to generate and design smaller-scale initiatives.
The Paris Agreement cemented an entirely new framework for the global climate regime, one based on countries’ voluntary emissions limitation plans (nationally determined contributions or NDCs) and which includes participation by both developing and developed countries on a continuum of mitigation capability rather than a dichotomy. The previous framework, embodied in the principles of the UNFCCC and the Kyoto Protocol, aimed for universal agreement on economy-wide, binding, top-down quantitative targets and timetables for developed countries while excepting developing countries. Given the inherent difficulties in reaching international agreement under this approach (Stewart et al. 2013a), decomposition of climate policy into smaller problem-solving components was proposed as a crucial step toward effective global cooperation (Aldy and Stavins 2007; Victor 2011).
Despite the Paris Agreement being potentially the best agreement which could have been reached under the circumstances (Bodansky 2016), it is insufficiently ambitious to limit the global mean temperature increase to 2 °C (UNFCCC 2015)—additional climate action will be required. As the NDCs were supposedly the maximum that countries were able or willing to do individually, any additional action over the same period will be taken primarily by non-state actors or countries acting cooperatively in other contexts. And although there has been increased climate action by non-state actors, this has not happened at a convincing scale in the context of a 2 °C objective.
While the concept of bottom-up action on climate is not novel—it was much discussed during the lull in the international negotiations and international climate action in advance of Copenhagen (Aldy and Stavins 2007)—the Paris Agreement is novel in that it brings disaggregated bottom-up action into an international framework. Some climate negotiators have suggested that a focus on non-state actors will reduce the pressure on countries to fulfill their NDCs and to make more ambitious commitments in the future. Therefore, to be most effective, the additional action should seek to strengthen the international regime created in Paris.
This Special Issue is focused on how to incentivize this additional climate action through a building blocks strategy and to do so in a way that enhances the Paris Agreement. The building blocks strategy is a bottom-up strategy designed to create an array of smaller-scale, specialized initiatives for transnational cooperation in particular sectors and/or geographic areas with a wide range of participants—private sector, NGOs, international organizations, and subnational and local governments, as well as national governments. Key to the building blocks initiatives are a focus on the non-climate incentives of actions that also have a climate benefit. Such incentives include private economic benefits, reduction of local and regional air pollution, economic development and energy security for countries, and mission advancement for development funders.
The building blocks strategy provides not only a theory of action but also a template for designing these initiatives and offers suggestions on how to generate momentum to develop these initiatives. It can strengthen the Paris Agreement in three ways: (1) by providing additional climate action by state and non-state actors in the short term; (2) by understanding opportunities and costs of increased action, it can help countries to increase the ambition in subsequent NDCs; and (3) by developing transparency and monitoring systems in building blocks initiatives, they may provide best practices and examples for the development of similar systems under the Paris Agreement, which are currently thinly defined (Bodansky 2016).
In this introduction to the Special Issue, we first provide a summary of the theory of building blocks that we have developed in greater detail in several earlier publications (Stewart et al. 2013a, b, 2015). We articulate the specific institutional logics of the building blocks strategy—clubs, institutional linkages, and dominant market actors—providing examples of initiatives that follow these logics. As well, we introduce the other papers of the Special Issue, noting how they complement and extend the building blocks proposal. Finally, we discuss how the building blocks can enhance the Paris Agreement and how the Paris Agreement can, in turn, enhance the development and stability of building
The present study examines the fidelity of outer tropical cyclone (TC) size and wind field structure in four atmospheric reanalysis datasets to evaluate whether reanalyses can be used to derive a long-term TC size dataset. Specifically, the precision and accuracy of reanalysis TC size for the North Atlantic (NA) and western North Pacific (WNP) basins are analyzed through comparison with a recently developed QuikSCAT TC size dataset (2000–09). Both outer TC size and structure in reanalyses closely match QuikSCAT data as revealed by strong correlations, similar standard deviations, and generally small biases. Of the TC size metrics examined, the radii of 6–8 m s−1 winds in the NA and radii of 6–10 m s−1 winds in the WNP are generally most comparable to QuikSCAT data. Compared to WNP TCs, NA TC size and structure are represented with greater fidelity. Among the four reanalyses examined, the National Centers for Environmental Prediction Climate Forecast System Reanalysis and the Japan Meteorological Agency Japanese 55-year Reanalysis represent TC size and structure with the greatest fidelity for both basins. Differences between reanalysis and QuikSCAT TC size increase with increasing QuikSCAT TC size in both basins and with decreasing TC latitude in the WNP. Finally, comparison of the distribution of reanalysis TC size during the satellite era with the distribution of QuikSCAT TC size suggests that reanalysis TC size is represented with reasonable fidelity throughout the satellite era and, thus, may be useful for constructing a multidecadal TC si
The reasons for concern framework communicates scientific understanding about risks in relation to varying levels of climate change. The framework, now a cornerstone of the IPCC assessments, aggregates global risks into five categories as a function of global mean temperature change. We review the framework’s conceptual basis and the risk judgments made in the most recent IPCC report, confirming those judgments in most cases in the light of more recent literature and identifying their limitations. We point to extensions of the framework that offer complementary climate change metrics to global mean temperature change and better account for possible changes in social and ecological system vulnerability. Further research should systematically evaluate risks under alternative scenarios of future climatic and societal conditions.
Richard L. Revesz,, Jason A. Schwartz, Peter H. Howard, Kenneth Arrow, Michael A. Livermore, Michael Oppenheimer, and Thomas Sterner. 2017. “Letter- The Social Cost of Carbon: A Global Imperative.” Review of Environmental Economics and Policy Vol. 11 (Issue 1): pp. 172-173.
Ocko, Ilissa B., Steven P. Hamburg, Daniel J. Jacob, David W. Keith, Nathaniel O. Keohane, Michael Oppenheimer, Joseph D. Roy-Mayhew, Daniel P. Schrag, and Stephen W. Pacala. 2017. “Unmask temporal trade-offs in climate policy debates.” Science Magazine vol.356 (Issue 6337): pp.492-493.
Estimates of climate change damage are central to the design of climate policies. Here, we develop a flexible architecture for computing damages that integrates climate science, econometric analyses, and process models. We use this approach to construct spatially explicit, probabilistic, and empirically derived estimates of economic damage in the United States from climate change. The combined value of market and nonmarket damage across analyzed sectors—agriculture, crime, coastal storms, energy, human mortality, and labor—increases quadratically in global mean temperature, costing roughly 1.2% of gross domestic product per +1°C on average. Importantly, risk is distributed unequally across locations, generating a large transfer of value northward and westward that increases economic inequality. By the late 21st century, the poorest third of counties are projected to experience damages between 2 and 20% of county income (90% chance) under business-as-usual emissions (Representative Concentration Pathway 8.5).
The amplification of flood frequencies by sea level rise (SLR) is expected to become one of the most economically damaging impacts of climate change for many coastal locations. Understanding the magnitude and pattern by which the frequency of current flood levels increase is important for developing more resilient coastal settlements, particularly since flood risk management (e.g. infrastructure, insurance, communications) is often tied to estimates of flood return periods. The Intergovernmental Panel on Climate Change's Fifth Assessment Report characterized the multiplication factor by which the frequency of flooding of a given height increases (referred to here as an amplification factor; AF). However, this characterization neither rigorously considered uncertainty in SLR nor distinguished between the amplification of different flooding levels (such as the 10% versus 0.2% annual chance floods); therefore, it may be seriously misleading. Because both historical flood frequency and projected SLR are uncertain, we combine joint probability distributions of the two to calculate AFs and their uncertainties over time. Under probabilistic relative sea level projections, while maintaining storm frequency fixed, we estimate a median 40-fold increase (ranging from 1- to 1314-fold) in the expected annual number of local 100-year floods for tide-gauge locations along the contiguous US coastline by 2050. While some places can expect disproportionate amplification of higher frequency events and thus primarily a greater number of historically precedented floods, others face amplification of lower frequency events and thus a particularly fast growing risk of historically unprecedented flooding. For example, with 50 cm of SLR, the 10%, 1%, and 0.2% annual chance floods are expected respectively to recur 108, 335, and 814 times as often in Seattle, but 148, 16, and 4 times as often in Charleston, SC.
The Intergovernmental Panel on Climate Change (IPCC) conducts policy-relevant but not policy-prescriptive assessments of climate science. In this review, we engage with some of the key design features, achievements, and challenges that situate and characterize the IPCC as an intergovernmental organization that is tasked with producing global environmental assessments (GEAs). These include the process of working through consensus to assess and summarize climate science and the need to include knowledge from as many of the 195 IPCC nation-states as possible, despite the structural inequalities between developed and developing countries. To highlight salient features that are unique to the IPCC but that offer lessons for other organizations that conduct GEAs, we include case studies on the politics of climate denialism, the use of geoengineering in mitigation scenarios, and the links between adaptive capacity, adaptation, and global development. We conclude with a discussion of institutional reflexivity. We consider how the IPCC can model an ethical and participatory response to climate change by critically examining, and being transparent about, the relation between science and politics.
Coastal flood protection measures have been widely implemented to improve flood resilience. However, protection levels vary among coastal megacities globally. This study compares the distinct flood protection standards for two coastal megacities, New York City and Shanghai, and investigates potential influences such as risk factors and past flood events. Extreme value analysis reveals that, compared to NYC, Shanghai faces a significantly higher flood hazard. Flood inundation analysis indicates that Shanghai has a higher exposure to extreme flooding. Meanwhile, Shanghai's urban development, population, and economy have increased much faster than NYC's over the last three decades. These risk factors provide part of the explanation for the implementation of a relatively high level of protection (e.g. reinforced concrete sea-wall designed for a 200-year flood return level) in Shanghai and low protection (e.g. vertical brick and stone walls and sand dunes) in NYC. However, individual extreme flood events (typhoons in 1962, 1974, and 1981) seem to have had a greater impact on flood protection decision-making in Shanghai, while NYC responded significantly less to past events (with the exception of Hurricane Sandy). Climate change, sea level rise, and ongoing coastal development are rapidly changing the hazard and risk calculus for both cities and both would benefit from a more systematic and dynamic approach to coastal protection.
While there is considerable interest in understanding the climate–migration relationship, particularly in the context of concerns about global climatic change, little is known about its underlying mechanisms. In the paper, we combine a rich panel data on annual bilateral international migration flows with an extensive data on climate variability across the countries to investigate in-depth the climate–migration link. We find a positive and statistically significant relationship between temperature and international outmigration only in the most agriculture-dependent countries, consistent with the widely documented adverse impact of temperature on agricultural productivity. Further, the temperature–migration relationship is nonlinear and resembles the nonlinear temperature–yield relationship. In addition, migration flows to current major destinations are especially temperature-sensitive. Policies to address issues related to climate-induced international migration would be more efficient if focused on the agriculture-dependent countries and especially people in those countries whose livelihoods depend on agriculture.
Expert judgement is an unavoidable element of the process-based numerical models used for climate change projections, and the statistical approaches used to characterize uncertainty across model ensembles. Here, we highlight the need for formalized approaches to unifying numerical modelling with expert judgement in order to facilitate characterization of uncertainty in a reproducible, consistent and transparent fashion. As an example, we use probabilistic inversion, a well-established technique used in many other applications outside of climate change, to fuse two recent analyses of twenty-first century Antarctic ice loss. Probabilistic inversion is but one of many possible approaches to formalizing the role of expert judgement, and the Antarctic ice sheet is only one possible climate-related application. We recommend indicators or signposts that characterize successful science-based uncertainty quantification.
Hallegatte, Stephane, Joeri Rogelj, Myles Allen, Leon Clarke, Ottmar Edenhofer, Christopher B. Field, Pierre Friedlingstein, et al. 2016. “Mapping the climate change challenge.” Nature Climate Change Vol. 6: pp.663-668. Abstract
Discussions on a long-term global goal to limit climate change, in the form of an upper limit to warming, were only partially resolved at the United Nations Framework Convention on Climate Change negotiations in Paris, 2015. Such a political agreement must be informed by scientific knowledge. One way to communicate the costs and benefits of policies is through a mapping that systematically explores the consequences of different choices. Such a multi-disciplinary effort based on the analysis of a set of scenarios helped structure the IPCC AR5 Synthesis Report. This Perspective summarizes this approach, reviews its strengths and limitations, and discusses how decision-makers can use its results in practice. It also identifies research needs that can facilitate integrated analysis of climate change and help better inform policy-makers and the public.
This work investigates the impact of climate variability on internal migration flows in post-apartheid South Africa. We combine information from South African censuses and climatic data to build a panel database covering the waves 1997–2001 and 2007–2011. The database enables the examination of the effect of spatiotemporal variability in temperature and precipitation on inter-district migration flows defined by five-year intervals. We employ a gravity approach where bilateral migration flows are explained by climate variability at the origin, along with a number of geographic, socio-economic and demographic factors traditionally identified as potential drivers of migration. Overall, we find that an increase in positive temperature extremes as well as positive and negative excess rainfall at the origin act as a push effect and enhance out-migration. However, the significance of the effect of climate on migration greatly varies by migrant characteristics. Particularly, flows of black and low-income South African migrants are strongly influenced by climatic variables whereas those of white and high-income migrants exhibit a weak impact. We also argue that agriculture may function as a transmission channel through which adverse climatic conditions affect migration.
Estimates of future flood hazards made under the assumption of stationary mean sea level are biased low due to sea-level rise (SLR). However, adjustments to flood return levels made assuming fixed increases of sea level are also inadequate when applied to sea level that is rising over time at an uncertain rate. SLR allowances—the height adjustment from historic flood levels that maintain under uncertainty the annual expected probability of flooding—are typically estimated independently of individual decision-makers’ preferences, such as time horizon, risk tolerance, and confidence in SLR projections. We provide a framework of SLR allowances that employs complete probability distributions of local SLR and a range of user-defined flood risk management preferences. Given non-stationary and uncertain sea-level rise, these metrics provide estimates of flood protection heights and offsets for different planning horizons in coastal areas. We illustrate the calculation of various allowance types for a set of long-duration tide gauges along U.S. coastlines.
The Paris Climate Agreement of December 2015 marks a decisive break from the unsuccessful Kyoto regime. Instead of targets and timetables, it established a Pledge and Review system, under which states will offer Nationally Determined Contributions (INDCs) to reducing emissions that cause climate change. But this successful negotiation outcome was achieved at the price of vagueness of obligations and substantial discretion for governments. Many governments will be tempted to use the vagueness of the Paris Agreement, and the discretion that it permits, to limit the scope or intensity of their proposed actions. Whether Pledge and Review under the Paris Agreement will lead to effective action against climate change will therefore depend on the inclination both of OECD countries and newly industrializing countries to take costly actions, which for the OECD countries will include financial transfers to their poorer partners. Domestic politics will be crucial in determining the attitudes of both sets of countries to pay such costs. The actual impact of the Paris Agreement will depend on whether it can be used by domestic groups favoring climate action as a point of leverage in domestic politics—that is, in a “two-level game” simultaneously involving both international and domestic politics.
Recent estimates suggest that global mean sea level rise could exceed 2 m by 2100. These projections are higher than previous ones and are based on the latest understanding of how the Antarctic Ice Sheet has behaved in the past and how sensitive it is to future climate change. They pose a challenge for scientists and policy-makers alike, requiring far-reaching decisions about coastal policies to be made based on rapidly evolving projections with large, persistent uncertainties. An effective approach to managing coastal risk should couple research priorities to policy needs, enabling judicious decision-making while focusing research on key questions.
This study investigates the effects of climatic variations and extremes captured by variability in temperature, precipitation, and incidents of typhoons on aggregate inter-provincial migration within the Philippines using panel data. Our results indicate that a rise in temperature and to some extent increased typhoonactivity increase outmigration, while precipitation does not have a consistent, signiﬁcant effect. We also ﬁnd that temperature and typhoons have signiﬁcant negative effects on rice yields, a proxy for agricultural productivity, and generate more outmigration from provinces that are more agriculturally dependent and have a larger share of rural population. Finally, migration decisions of males, younger individuals, and those with higher levels of education are more sensitive to rising temperature and typhoons. We conclude that temperature increase and to some extent typhoon activities promote migration, potentially through their negative effect on crop yields. The migration responses of males, more educated, and younger individuals are more sensitive to these climatic impacts
The representative concentration pathway (RCP) simulations included in phase 5 of the Coupled Model Intercomparison Project (CMIP5) quantify the response of the climate system to different natural and anthropogenic forcing scenarios. These simulations differ because of 1) forcing, 2) the representation of the climate system in atmosphere–ocean general circulation models (AOGCMs), and 3) the presence of unforced (internal) variability. Global and local sea level rise projections derived from these simulations, and the emergence of distinct responses to the four RCPs depend on the relative magnitude of these sources of uncertainty at different lead times. Here, the uncertainty in CMIP5 projections of sea level is partitioned at global and local scales, using a 164-member ensemble of twenty-first-century simulations. Local projections at New York City (NYSL) are highlighted. The partition between model uncertainty, scenario uncertainty, and internal variability in global mean sea level (GMSL) is qualitatively consistent with that of surface air temperature, with model uncertainty dominant for most of the twenty-first century. Locally, model uncertainty is dominant through 2100, with maxima in the North Atlantic and the Arctic Ocean. The model spread is driven largely by 4 of the 16 AOGCMs in the ensemble; these models exhibit outlying behavior in all RCPs and in both GMSL and NYSL. The magnitude of internal variability varies widely by location and across models, leading to differences of several decades in the local emergence of RCPs. The AOGCM spread, and its sensitivity to model exclusion and/or weighting, has important implications for sea level assessments, especially if a local risk management approach is utilized.
Future coastal flood risk will be strongly influenced by sea-level rise (SLR) and changes in the frequency and intensity of tropical cyclones. These two factors are generally considered independently. Here, we assess twenty-first century changes in the coastal hazard for the US East Coast using a flood index (FI) that accounts for changes in flood duration and magnitude driven by SLR and changes in power dissipation index (PDI, an integrated measure of tropical cyclone intensity, frequency and duration). Sea-level rise and PDI are derived from representative concentration pathway (RCP) simulations of 15 atmosphere–ocean general circulation models (AOGCMs). By 2080–2099, projected changes in the FI relative to 1986–2005 are substantial and positively skewed: a 10th–90th percentile range 4–75 times higher for RCP 2.6 and 35–350 times higher for RCP 8.5. High-end FI projections are driven by three AOGCMs that project the largest increases in SLR, PDI and upper ocean temperatures. Changes in PDI are particularly influential if their intra-model correlation with SLR is included, increasing the RCP 8.5 90th percentile FI by a further 25%. Sea-level rise from other, possibly correlated, climate processes (for example, ice sheet and glacier mass changes) will further increase coastal flood risk and should be accounted for in comprehensive assessme
Climate change and resulting sea level rise will cause risk from coastal storms to increase throughout this century. Aggressive implementation of emissions reduction policies would significantly limit the risk but in any event, planning for comprehensive adaptation is necessary. Past experience with long term planning to reduce vulnerability and exposure along the coast shows a significant shortfall between the need to reduce risk and the implementation of appropriate policies. A new approach to public policy in this arena should be a priority for policy makers.
A central task for negotiators of the post-2020 global climate agreement is to construct a finance regime that supports low-carbon development in developing economies. As power sector investments between developing countries grow, the climate finance regime should incentivize the decarbonization of these major sources of finance by integrating them as a complement to the commitments of developed nations. The emergence of the Asian Infrastructure Investment Bank, South–South Cooperation Fund and other nascent institutions reveal the fissures that exist in rules and norms surrounding international finance in the power sector. Structuring the climate agreement in Paris to credit qualified finance from the developing world could have several advantages, including: (1) encouraging low-carbon cooperation between developing countries; (2) incentivizing emerging investors to prefer low-carbon investments; and (3) enabling more cost-effective attainment of national and global climate objectives. Failure to coordinate on standards now could hinder low-carbon development in the decades to come.
This paper explores the governance options surrounding geoengineering—the deliberate, large-scale manipulation of the Earth’s climate system to counteract climate change. The authors focus solely on methods that affect the incoming solar radiation to the atmosphere, referred to as solar radiation management (SRM). They examine whether an international governance framework for SRM is needed, how it should be designed, and whether it is feasible. The authors propose a governance regime that initially has small membership and weak legalization, and is flexible in that future institutional reforms allow for broader membership and deeper commitments. The article provides supporting evidence for key aspects of the regime through past international treaties in arms control and environmental protection, including the Antarctica, Outer Space, and Montreal Protocol treaty regimes. For these cases, acting early and treating the respective problems as part of the “regulation of unexplored territory” produced more effective outcomes than the “national appropriation” approach that characterizes arms control.
Researchers have extensively studied crop yield response to weather variations, while only a limited number of studies have attempted to identify spatial heterogeneity in this relationship. We explore spatial heterogeneity in corn yield response to weather by combining geographically weighted regression and panel regression. We find that temperature tends to have negative effects on U.S. corn yields in warmer regions and positive effects in cooler regions, with spatial heterogeneity at a fine scale. The spatial pattern of precipitation effects is more complicated. A further analysis shows that precipitation effects are sensitive to the existence of irrigation systems.
We present a microlevel study to simultaneously investigate the effects of variations in temperature and precipitation along with sudden natural disasters to infer their relative influence on migration that is likely permanent. The study is made possible by the availability of household panel data from Indonesia with an exceptional tracking rate combined with frequent occurrence of natural disasters and significant climatic variations, thus providing a quasi-experiment to examine the influence of environment on migration. Using data on 7,185 households followed over 15 y, we analyze whole-household, province-to-province migration, which allows us to understand the effects of environmental factors on permanent moves that may differ from temporary migration. The results suggest that permanent migration is influenced by climatic variations, whereas episodic disasters tend to have much smaller or no impact on such migration. In particular, temperature has a nonlinear effect on migration such that above 25 °C, a rise in temperature is related to an increase in outmigration, potentially through its impact on economic conditions. We use these results to estimate the impact of projected temperature increases on future permanent migration. Though precipitation also has a similar nonlinear effect on migration, the effect is smaller than that of temperature, underscoring the importance of using an expanded set of climatic factors as predictors of migration. These findings on the minimal influence of natural disasters and precipitation on permanent moves supplement previous findings on the significant role of these variables in promoting temporary migration.
Communication by scientists with policy makers and attentive publics raises ethical issues. Scientists need to decide how to communicate knowledge effectively in a way that nonscientists can understand and use, while remaining honest scientists and presenting estimates of the uncertainty of their inferences. They need to understand their own ethical choices in using scientific information to communicate to audiences. These issues were salient in the Fourth Assessment of the Intergovernmental Panel on Climate Change with respect to possible sea level rise from disintegration of the Greenland and West Antarctic ice sheets. Due to uncertainty, the reported values of projected sea level rise were incomplete, potentially leading some relevant audiences to underestimate future risk. Such judgments should be made in a principled rather than an ad hoc manner. Five principles for scientific communication under such conditions are important: honesty, precision, audience relevance, process transparency, and specification of uncertainty about conclusions. Some of these principles are of intrinsic importance while others are merely instrumental and subject to trade-offs among them. Scientists engaged in assessments under uncertainty should understand these principles and which trade-offs are acceptable.
Sea‐level rise due to both climate change and non‐climatic factors threatens coastal settlements, infrastructure, and ecosystems. Projections of mean global sea‐level (GSL) rise provide insufficient information to plan adaptive responses; local decisions require local projections that accommodate different risk tolerances and time frames and that can be linked to storm surge projections. Here we present a global set of local sea‐level (LSL) projections to inform decisions on timescales ranging from the coming decades through the 22nd century. We provide complete probability distributions, informed by a combination of expert community assessment, expert elicitation, and process modeling. Between the years 2000 and 2100, we project a very likely (90% probability) GSL rise of 0.5–1.2 m under representative concentration pathway (RCP) 8.5, 0.4–0.9 m under RCP 4.5, and 0.3–0.8 m under RCP 2.6. Site‐to‐site differences in LSL projections are due to varying non‐climatic background uplift or subsidence, oceanographic effects, and spatially variable responses of the geoid and the lithosphere to shrinking land ice. The Antarctic ice sheet (AIS) constitutes a growing share of variance in GSL and LSL projections. In the global average and at many locations, it is the dominant source of variance in late 21st century projections, though at some sites oceanographic processes contribute the largest share throughout the century. LSL rise dramatically reshapes flood risk, greatly increasing the expected number of “1‐in‐10” and “1‐in‐100” year events.
Previous research on the determinants of international migration has largely focused on objective factors, such as income. We instead use subjective well-being (SWB) to explain international migration desires, an expressed willingness to migrate. We find that individuals with higher SWB have lower international migration desires. At the individual level, the SWB-migration relationship appears to be more robust than the income-migration relationship. At the country level, national average SWB better indicates international migration desires for rich countries, while income performs better for poor countries. We thus demonstrate the feasibility of employing subjective measures to study at least one aspect of an important social outcome, migration.
Mitigation of the urban heat island (UHI) effect at the city-scale is investigated using the Weather Research and Forecasting (WRF) model in conjunction with the Princeton Urban Canopy Model (PUCM). Specifically, the cooling impacts of green roof and cool (white/high-albedo) roof strategies over the Baltimore-Washington metropolitan area during a heat wave period (7 June–10 June 2008) are assessed using the optimal set-up of WRF-PUCM described in the companion paper by Li and Bou-Zeid (2014). Results indicate that the surface UHI effect (defined based on the urban–rural surface temperature difference) is reduced significantly more than the near-surface UHI effect (defined based on urban–rural 2 m air temperature difference) when these mitigation strategies are adopted. In addition, as the green and cool roof fractions increase, the surface and near-surface UHIs are reduced almost linearly. Green roofs with relatively abundant soil moisture have comparable effect in reducing the surface and near-surface UHIs to cool roofs with an albedo value of 0.7. Significant indirect effects are also observed for both green and cool roof strategies; mainly, the low-level advection of atmospheric moisture from rural areas into urban terrain is enhanced when the fraction of these roofs increases, thus increasing the humidity in urban areas. The additional benefits or penalties associated with modifications of the main physical determinants of green or cool roof performance are also investigated. For green roofs, when the soil moisture is increased by irrigation, additional cooling effect is obtained, especially when the 'unmanaged' soil moisture is low. The effects of changing the albedo of cool roofs are also substantial. These results also underline the capabilities of the WRF-PUCM framework to support detailed analysis and diagnosis of the UHI phenomenon, and of its different mitigation strategies.
Richard L. Revesz,, Peter H. Howard, Kenneth Arrow, Lawrence H.Gouldner, Robert E. Kopp, Michael A. Livermore, Michael Oppenheimer, and Thomas Sterner. 2014. “ Improve economic models of climate change.” Nature Vol 508 (Issue 7495): pp.173-175.
Crop model‐specific biases are a key uncertainty affecting our understanding of climate change impacts to agriculture. There is increasing research focus on intermodel variation, but comparisons between mechanistic (MM s) and empirical models (EM s) are rare despite both being used widely in this field. We combined MM s and EM s to project future (2055) changes in the potential distribution (suitability) and productivity of maize and spring wheat in S outh A frica under 18 downscaled climate scenarios (9 models run under 2 emissions scenarios). EM s projected larger yield losses or smaller gains than MM s. The EM s’ median‐projected maize and wheat yield changes were −3.6% and 6.2%, respectively, compared to 6.5% and 15.2% for the MM . The EM projected a 10% reduction in the potential maize growing area, where the MM projected a 9% gain. Both models showed increases in the potential spring wheat production region (EM = 48%, MM = 20%), but these results were more equivocal because both models (particularly the EM ) substantially overestimated the extent of current suitability. The substantial water‐use efficiency gains simulated by the MM s under elevated CO 2 accounted for much of the EM −MM difference, but EM s may have more accurately represented crop temperature sensitivities. Our results align with earlier studies showing that EM s may show larger climate change losses than MM s. Crop forecasting efforts should expand to include EM −MM comparisons to provide a fuller picture of crop–climate response uncertainties.
This Article presents an innovative institutional strategy for global climate protection, quite distinct from, but ultimately complementary to and supportive of the currently stalled UNFCCC climate treaty negotiations. The bottom-up strategy relies on a variety of smallerscale transnational cooperative arrangements, involving not only states but sub-national jurisdictions, firms, and CSOs, to undertake activities whose primary goal is not climate mitigation but which will achieve greenhouse gas reductions as an inherent byproduct. This strategy avoids the inherent problems in securing an enforceable treaty to secure the global public good of climate protection by mobilizing other incentives - including economic self-interest, energy security, cleaner air, and furtherance of international development - to motivate such actors to cooperate on actions that will also benefit the climate. These bottom-up regimes will contribute to global climate action not only by achieving emissions reductions in the short-term, but also by linking the bottom-up regimes to the UNFCCC system through greenhouse gas monitoring and reporting systems. In these ways, the bottom-up strategy will help secure eventual agreement on a global climate treaty.
Carbon capture and sequestration (CCS) may play a key role in our energy future. However, the widespread sequestration of CO2 into storage reservoirs is inhibited by safety and leakage concerns. Effective leakage monitoring at the surface is recently made possible by the development of quantum cascade (QC) laser-based sensors, which are capable of tracking fluxes in CO2 isotope concentrations. In this paper, we initially discuss the status of this technology, including recent results from distributed feedback QC lasers for use in sensing CO2 isotopic ratios. These lasers show single-mode emission at 4.32 μm, overlapping strong absorption resonances of 12CO2, 13CO2, and 18OCO. We then consider the value of such devices for quantifying CO2 leakage using a climate-economy integrated-assessment model that is modified to include CCS. The sensitivity of model outcomes to reservoir leakage is studied, showing that an average reservoir storage half-life on the order of 1000 years or longer can limit atmospheric temperature increases to 2°C or less over the next 150 years for economically optimal emissions scenarios. The present day economic value of CCS is established versus reservoir half-life, showing a significant return on investment ( ~ 2 trillion U.S.$, or ~ 4% of gross world product) when the average reservoir half-life is 250 years, with a sharp drop in the value of CCS technology for half-life values below 250 years. Quantifying CO2 leakage rates via QC laser-based sensing will contribute greatly toward accurately assessing CCS technology and its efficacy as part of CO2 limitation strategies.
Much of the biodiversity‐related climate change impacts research has focused on the direct effects to species and ecosystems. Far less attention has been paid to the potential ecological consequences of human efforts to address the effects of climate change, which may equal or exceed the direct effects of climate change on biodiversity. One of the most significant human responses is likely to be mediated through changes in the agricultural utility of land. As farmers adapt their practices to changing climates, they may increase pressure on some areas that are important to conserve (conservation lands) whereas lessening it on others. We quantified how the agricultural utility of South African conservation lands may be altered by climate change. We assumed that the probability of an area being farmed is linked to the economic benefits of doing so, using land productivity values to represent production benefit and topographic ruggedness as a proxy for costs associated with mechanical workability. We computed current and future values of maize and wheat production in key conservation lands using the DSSAT4.5 model and 36 crop‐climate response scenarios. Most conservation lands had, and were predicted to continue to have, low agricultural utility because of their location in rugged terrain. However, several areas were predicted to maintain or gain high agricultural utility and may therefore be at risk of near‐term or future conversion to cropland. Conversely, some areas were predicted to decrease in agricultural utility and may therefore prove easier to protect from conversion. Our study provides an approximate but readily transferable method for incorporating potential human responses to climate change into conservation planning.
This essay proposes an innovative institutional strategy for global climate protection, quite distinct from but ultimately complementary to the UNFCCC climate treaty negotiations. Our “building block” strategy relies on a variety of smaller-scale transnational cooperative arrangements, involving not only states, but also subnational jurisdictions, firms, and civil society organizations, to undertake activities whose primary goal is not climate mitigation but which will achieve greenhouse gas reductions as a byproduct. This strategy avoids the problems inherent in developing an enforceable, comprehensive treaty regime by mobilizing other incentives—including economic self-interest, energy security, cleaner air, and furtherance of international development— to motivate a range of actors to cooperate on actions that will also produce climate benefits. The strategy uses three specific models of regime formation (club, linkage, and dominant actor models) which emerge from economics, international relations, and organizational behavior, to develop a variety of transnational regimes that are generally self-enforcing and sustainable, avoiding the free rider and compliance problems endemic in collective action to provide public goods. These regimes will contribute to global climate action not only by achieving emissions reductions in the short term, but also by creating global webs of cooperation and trust, and by linking the building block regimes to the UNFCCC system through greenhouse gas monitoring and reporting systems. We argue that the building blocks regimes would thereby help secure eventual agreement on a comprehensive climate treaty.
Climate adaptation and flood risk assessments1,2 have incorporated sea-level rise (SLR) projections developed using semi-empirical methods3,4,5 (SEMs) and expert-informed mass-balance scenarios2,6. These techniques, which do not explicitly model ice dynamics, generate upper bounds on twenty-first century SLR that are up to three times higher than Intergovernmental Panel on Climate Change estimates7. However, the physical basis underlying these projections, and their likelihood of occurrence, remain unclear8,9,10. Here, we develop mass-balance projections for the Antarctic ice sheet within a Bayesian probabilistic framework10, integrating numerical model output11 and updating projections with an observational synthesis12. Without abrupt, sustained, changes in ice discharge (collapse), we project a 95th percentile mass loss equivalent to ∼13 cm SLR by 2100, lower than previous upper-bound projections. Substantially higher mass loss requires regional collapse, invoking dynamics that are likely to be inconsistent with the underlying assumptions of SEMs. In this probabilistic framework, the pronounced sensitivity of upper-bound SLR projections to the poorly known likelihood of collapse is lessened with constraints on the persistence and magnitude of subsequent discharge. More realistic, fully probabilistic, estimates of the ice-sheet contribution to SLR may thus be obtained by assimilating additional observations and numerical models.
Previous sea level rise (SLR) assessments have excluded the potential for dynamic ice loss over much of Greenland and Antarctica, and recently proposed “upper bounds” on Antarctica’s 21st-century SLR contribution are derived principally from regions where present-day mass loss is concentrated (basin 15, or B15, drained largely by Pine Island, Thwaites, and Smith glaciers). Here, we present a probabilistic framework for assessing the ice sheet contribution to sea level change that explicitly accounts for mass balance uncertainty over an entire ice sheet. Applying this framework to Antarctica, we find that ongoing mass imbalances in non-B15 basins give an SLR contribution by 2100 that: (i) is comparable to projected changes in B15 discharge and Antarctica’s surface mass balance, and (ii) varies widely depending on the subset of basins and observational dataset used in projections. Increases in discharge uncertainty, or decreases in the exceedance probability used to define an upper bound, increase the fractional contribution of non-B15 basins; even weak spatial correlations in future discharge growth rates markedly enhance this sensitivity. Although these projections rely on poorly constrained statistical parameters, they may be updated with observations and/or models at many spatial scales, facilitating a more comprehensive account of uncertainty that, if implemented, will improve future assessments.
Intercomparison of mechanistic and empirical models is an important step towards improving projections of potential species distribution and abundance. We aim to compare suitability and productivity estimates for a well‐understood crop species to evaluate the strengths and weaknesses of mechanistic versus empirical modelling.
The last interglacial stage (LIG; ca. 130–115 ka) provides a relatively recent example of a world with both poles characterized by greater-than-Holocene temperatures similar to those expected later in this century under a range of greenhouse gas emission scenarios. Previous analyses inferred that LIG mean global sea level (GSL) peaked 6–9 m higher than today. Here, we extend our earlier work to perform a probabilistic assessment of sea level variability within the LIG highstand. Using the terminology for probability employed in the Intergovernmental Panel on Climate Change assessment reports, we find it extremely likely (95 per cent probability) that the palaeo-sea level record allows resolution of at least two intra-LIG sea level peaks and likely (67 per cent probability) that the magnitude of low-to-high swings exceeded 4 m. Moreover, it is likely that there was a period during the LIG in which GSL rose at a 1000-yr average rate exceeding 3 m kyr−1, but unlikely (33 per cent probability) that the rate exceeded 7 m kyr−1 and extremely unlikely (5 per cent probability) that it exceeded 11 m kyr−1. These rate estimates can provide insight into rates of Greenland and/or Antarctic melt under climate conditions partially analogous to those expected in the 21st century.
 A coupled ice stream‐ice shelf‐ocean cavity model is used to assess the sensitivity of the coupled system to far‐field ocean temperatures, varying from 0.0 to 1.8°C, as well as sensitivity to the parameters controlling grounded ice flow. A response to warming is seen in grounding line retreat and grounded ice loss that cannot be inferred from the response of integrated melt rates alone. This is due to concentrated thinning at the ice shelf lateral margin, and to processes that contribute to this thinning. Parameters controlling the flow of grounded ice have a strong influence on the response to sub‐ice shelf melting, but this influence is not seen until several years after an initial perturbation in temperatures. The simulated melt rates are on the order of that observed for Pine Island Glacier in the 1990s. However, retreat rates are much slower, possibly due to unrepresented bedrock features.
 Antarctic ice shelves interact closely with the ocean cavities beneath them, with ice shelf geometry influencing ocean cavity circulation, and heat from the ocean driving changes in the ice shelves, as well as the grounded ice streams that feed them. We present a new coupled model of an ice stream‐ice shelf‐ocean system that is used to study this interaction. The model is capable of representing a moving grounding line and dynamically responding ocean circulation within the ice shelf cavity. Idealized experiments designed to investigate the response of the coupled system to instantaneous increases in ocean temperature show ice‐ocean system responses on multiple timescales. Melt rates and ice shelf basal slopes near the grounding line adjust in 1–2 years, and downstream advection of the resulting ice shelf thinning takes place on decadal timescales. Retreat of the grounding line and adjustment of grounded ice takes place on a much longer timescale, and the system takes several centuries to reach a new steady state. During this slow retreat, and in the absence of either an upward‐or downward‐sloping bed or long‐term trends in ocean heat content, the ice shelf and melt rates maintain a characteristic pattern relative to the grounding line.
Ice-shelf basal melting is tightly coupled to ice-shelf morphology. Ice shelves, in turn, are coupled to grounded ice via their influence on compressive stress at the grounding line (‘ice-shelf buttressing’). Here, we examine this interaction using a local parameterization that relates the basal melt rate to the ice-shelf thickness gradient. This formulation permits a closed-form solution for a steady-state ice tongue. Time-dependent numerical simulations reveal the spatial and temporal evolution of ice-shelf/ice-stream systems in response to changes in ocean temperature, and the influence of morphology-dependent melting on grounding-line retreat. We find that a rapid (<1 year) re-equilibration in upstream regions of ice shelves establishes a spatial pattern of basal melt rates (relative to the grounding line) that persists over centuries. Coupling melting to ice-shelf shape generally, but not always, increases grounding-line retreat rates relative to a uniform distribution with the same area- average melt rate. Because upstream ice-shelf thickness gradients and retreat rates increase nonlinearly with thermal forcing, morphology-dependent melting is more important to the response of weakly buttressed, strongly forced ice streams grounded on beds that slope upwards towards the ocean (e.g. those in the Amundsen Sea)
Over the past two decades, skeptics of the reality and signiﬁcance of anthropogenic climate change have frequently accused climate scientists of ‘‘alarmism’’: of over-interpreting or overreacting to evidence of human impacts on the climate system. However, the available evidence suggests that scientists have in fact been conservative in their projections of the impacts of climate change. In particular, we discuss recent studies showing that at least some of the key attributes of global warming from increased atmospheric greenhouse gases have been under-predicted, particularly in IPCC assessments of the physical science, by Working Group I. We also note the less frequent manifestation of over-prediction of key characteristics of climate in such assessments. We suggest, therefore, that scientists are biased not toward alarmism but rather the reverse: toward cautious estimates, where we deﬁne caution as erring on the side of less rather than more alarming predictions. We call this tendency ‘‘erring on the side of least drama (ESLD).’’ We explore some cases of ESLD at work, including predictions of Arctic ozone depletion and the possible disintegration of the West Antarctic ice sheet, and suggest some possible causes of this directional bias, including adherence to the scientiﬁc norms of restraint, objectivity, skepticism, rationality, dispassion, and moderation. We conclude with suggestions for further work to identify and explore ESLD.
Aim: Ecosystems face numerous well-documented threats from climate change. The well-being of people also is threatened by climate change, most prominently by reduced food security. Human adaptation to food scarcity, including shifting agricultural zones, will create new threats for natural ecosystems. We investigated how shifts in crop suitability because of climate change may overlap currently protected areas (PAs) and priority sites for PA expansion in South Africa. Predicting the locations of suitable climate conditions for crop growth will assist conservationists and decision-makers in planning for climate change. Location: South Africa. Methods: We modelled climatic suitability in 2055 for maize and wheat cultivation, two extensively planted, staple crops, and overlaid projected changes with PAs and PA expansion priorities. Results: Changes in winter climate could make an additional 2 million ha of land suitable for wheat cultivation, while changes in summer climate could expand maize suitability by up to 3.5 million ha. Conversely, 3 million ha of lands currently suitable for wheat production are predicted to become climatically unsuitable, along with 13 million ha for maize. At least 328 of 834 (39%) PAs are projected to be affected by altered wheat or maize suitability in their buffer zones. Main conclusions: Reduced crop suitability and food scarcity in subsistence areas may lead to the exploitation of PAs for food and fuel. However, if reduced crop suitability leads to agricultural abandonment, this may afford opportunities for ecological restoration. Expanded crop suitability in PA buffer zones could lead to additional isolation of PAs if portions of newly suitable land are converted to agriculture. These results suggest that altered crop suitability will be widespread throughout South Africa, including within and around lands identiﬁed as conservation priorities. Assessing how climate change will affect crop suitability near PAs is a ﬁrst step towards proactively identifying potential conﬂicts between human adaptation and conservation planning.
We investigate the link between agricultural productivity and net migration in the United States using a county-level panel for the most recent period of 1970-2009. In rural counties of the Corn Belt, we find a statistically significant relationship between changes in net outmigration and climate-driven changes in crop yields, with an estimated semi-elasticity of about -0.17, i.e., a 1% decrease in yields leads to a 0.17% net reduction of the population through migration. This effect is primarily driven by young adults. We do not detect a response for senior citizens, nor for the general population in eastern counties outside the Corn Belt. Applying this semi-elasticity to predicted yield changes under the B2 scenario of the Hadley III model, we project that, holding other factors constant, climate change would on average induce 3.7% of the adult population (ages 15-59) to leave rural counties of the Corn Belt in the medium term (2020-2049) compared to the 1960-1989 baseline, with the possibility of a much larger migration response in the long term (2077-2099). Since there is uncertainty about future warming, we also present projections for a range of uniform climate change scenarios in temperature or precipitation.
Storm surges are responsible for much of the damage and loss of life associated with landfalling hurricanes. Understanding how global warming will affect hurricane surges thus holds great interest. As general circulation models (GCMs) cannot simulate hurricane surges directly, we couple a GCM-driven hurricane model with hydrodynamic models to simulate large numbers of synthetic surge events under projected climates and assess surge threat, as an example, for New York City (NYC). Struck by many intense hurricanes in recorded history and prehistory, NYC is highly vulnerable to storm surges. We show that the change of storm climatology will probably increase the surge risk for NYC; results based on two GCMs show the distribution of surge levels shifting to higher values by a magnitude comparable to the projected sea-level rise (SLR). The combined effects of storm climatology change and a 1 m SLR may cause the present NYC 100-yr surge flooding to occur every 3–20 yr and the present 500-yr flooding to occur every 25–240 yr by the end of the century.
How and why did the scientific consensus about sea level rise due to the disintegration of the West Antarctic Ice Sheet (WAIS), expressed in the third Intergovernmental Panel on Climate Change (IPCC) assessment, disintegrate on the road to the fourth? Using ethnographic interviews and analysis of IPCC documents, we trace the abrupt disintegration of the WAIS consensus. First, we provide a brief historical overview of scientific assessments of the WAIS. Second, we provide a detailed case study of the decision not to provide a WAIS prediction in the Fourth Assessment Report. Third, we discuss the implications of this outcome for the general issue of scientists and policymakers working in assessment organizations to make projections. IPCC authors were less certain about potential WAIS futures than in previous assessment reports in part because of new information, but also because of the outcome of cultural processes within the IPCC, including how people were selected for and worked together within their writing groups. It became too difficult for IPCC assessors to project the range of possible futures for WAIS due to shifts in scientific knowledge as well as in the institutions that facilitated the interpretations of this knowledge.
The assessment of potential impacts of climate change is progressing from taxonomies and enumeration of the magnitude of potential direct effects on individuals, societies, species, and ecosystems according to a limited number of metrics toward a more integrated approach that also encompasses the vast range of human response to experience and risk. Recent advances are both conceptual and methodological, and include analysis of some consequences of climate change that were heretofore intractable. In this article, I review a selection of these developments and represent them through a handful of illustrative cases. A key characteristic of the emerging areas of interest is a focus on understanding how human responses to direct impacts of climate change may cause important indirect and sometimes distant impacts. This realization underscores the need to develop integrated approaches for assessing and modeling impacts in an evolving socioeconomic and policy context.
Sea level rise, especially combined with possible changes in storm surges and increased river discharge resulting from climate change, poses a major threat in low-lying river deltas. In this study we focus on a specific example of such a delta: the Netherlands. To evaluate whether the country’s flood protection strategy is capable of coping with future climate conditions, an assessment of low-probability/high-impact scenarios is conducted, focusing mainly on sea level rise. We develop a plausible high-end scenario of 0.55 to 1.15 m global mean sea level rise, and 0.40 to 1.05 m rise on the coast of the Netherlands by 2100 (excluding land subsidence), and more than three times these local values by 2200. Together with projections for changes in storm surge height and peak river discharge, these scenarios depict a complex, enhanced flood risk for the Dutch delta.
Abstract Large‐scale assessments have become an important vehicle for organizing, interpreting, and presenting scientific information relevant to environmental policy. At the same time, identifying and evaluating scientific uncertainty with respect to the very questions these assessments were designed to address has become more difficult, as ever more complex problems involving greater portions of the Earth system and longer timescales have emerged at the science–policy interface. In this article, we explore expert judgments about uncertainty in two recent cases: the assessment of stratospheric ozone depletion, and the assessment of the response of the West Antarctic ice sheet (WAIS) to global warming. These assessments were fairly adept at characterizing one type of uncertainty in models (parameter uncertainty), but faced much greater difficulty in dealing with structural model uncertainty, sometimes entirely avoiding grappling with it. In the absence of viable models, innovative approaches were developed in the ozone case for consolidating information about highly uncertain future outcomes, whereas little such progress has been made thus far in the case of WAIS. Both cases illustrate the problem of expert disagreement, suggesting that future assessments need to develop improved approaches to representing internal conflicts of judgment, in order to produce a more complete evaluation of uncertainty. WIREs Clim Change 2011 2 728–743 DOI: 10.1002/wcc.135 This article is categorized under: Integrated Assessment of Climate Change > Integrated Assessment by Expert Panels
Climate change poses profound, direct, and well‐documented threats to biodiversity. A significant fraction of Earth's species is at risk of extinction due to changing precipitation and temperature regimes, rising and acidifying oceans, and other factors. There is also growing awareness of the diversity and magnitude of responses, both proactive and reactive, that people will undertake as lives and livelihoods are affected by climate change. Yet to date few studies have examined the relationship between these two powerful forces. The natural systems upon which people depend, already under direct assault from climate change, are further threatened by how we respond to climate change. Human history and recent studies suggest that our actions to cope with climate change (adaptation) or lessen its rate and magnitude (mitigation) could have impacts that match—and even exceed—the direct effects of climate change on ecosystems. If we are to successfully conserve biodiversity and maintain ecosystem services in a warming world, considerable effort is needed to predict and reduce the indirect risks created by climate change.
Global anthropogenic changes in carbon (C) and nitrogen (N) cycles call for modeling tools that are able to address and quantify essential interactions between N, C, and climate in terrestrial ecosystems. Here we introduce a prognostic N cycle within the Princeton–Geophysical Fluid Dynamic Laboratory (GFDL) LM3V land model. The model captures mechanisms essential for N cycling and their feedbacks on C cycling: N limitation of plant productivity, the N dependence of C decomposition and stabilization in soils, removal of available N by competing sinks, ecosystem losses that include dissolved organic and volatile N, and ecosystem inputs through biological N fixation. Our model captures many essential characteristics of C‐N interactions and is capable of broadly recreating spatial and temporal variations in N and C dynamics. The introduced N dynamics improve the model's short‐term NPP response to step changes in CO2. Consistent with theories of successional dynamics, we find that physical disturbance induces strong C‐N feedbacks, caused by intermittent N loss and subsequent N limitation. In contrast, C‐N interactions are weak when the coupled model system approaches equilibrium. Thus, at steady state, many simulated features of the carbon cycle, such as primary productivity and carbon inventories, are similar to simulations that do not include C‐N feedbacks.
The average annual cost of floods in the United States has been estimated at about $2 billion (current US dollars). The federal government, through the creation of the National Flood Insurance Program (NFIP), has assumed responsibility for mitigating the societal and economic impacts of flooding by establishing a national policy that provides subsidized flood insurance. Increased flood costs during the past two decades have made the NFIP operate at a deficit. This paper argues that our current understanding of climate change and of the sensitivity of the urbanenvironmenttofloodscallforchangestothefloodpolicyscheme.Conclusions are drawn on specific examples from cities along the heavily urbanized corridor of northeastern United States. Mesoscale and global models along with urbanization and economic growth statistics are used to provide insights and recommendations for future flood costs under different emissions scenarios. Mesoscale modeling and future projections from global models suggest, for example, that under a high emissions scenario, New York City could experience almost twice as many days of extreme precipitation that cause flood damage and are disruptive to business as today. The results of the paper suggest that annual flood costs in the United States will increase sharply by the end of the 21st Century, ranging from about $7 to $19 billioncurrentUSdollars,dependingontheeconomicgrowthrateandtheemissions
Searchinger, Timothy, Steven Hamburg, Jerry Melillo, William Chameides, Petr Havlik, Daniel M. Kammen, Gene E. Likens, et al. 2010. “Carbon Calculations to Consider—Response.” Science vol 327 (issue 5967): pp.781.
Searchinger, Tim, Steven Hamburg, Jerry Melillo, William Chameides, Petr Havlik, Daniel M. Kammen, Gene E. Likens, et al. 2010. “Bioenergy: Counting on Incentives—Response.” Science vol 327 (issue 5970): pp.1200-1201.
Climate change is expected to cause mass human migration, including immigration across international borders. This study quantitatively examines the linkages among variations in climate, agricultural yields, and people's migration responses by using an instrumental variables approach. Our method allows us to identify the relationship between crop yields and migration without explicitly controlling for all other confounding factors. Using state-level data from Mexico, we find a significant effect of climate-driven changes in crop yields on the rate of emigration to the United States. The estimated semielasticity of emigration with respect to crop yields is approximately −0.2, i.e., a 10% reduction in crop yields would lead an additional 2% of the population to emigrate. We then use the estimated semielasticity to explore the potential magnitude of future emigration. Depending on the warming scenarios used and adaptation levels assumed, with other factors held constant, by approximately the year 2080, climate change is estimated to induce 1.4 to 6.7 million adult Mexicans (or 2% to 10% of the current population aged 15–65 y) to emigrate as a result of declines in agricultural productivity alone. Although the results cannot be mechanically extrapolated to other areas and time periods, our findings are significant from a global perspective given that many regions, especially developing countries, are expected to experience significant declines in agricultural yields as a result of projected warming.