Phytoplankton biomass substantially influences the Arctic climate via biogeophysical feedback, i.e., an increase in the mean chlorophyll concentration absorbs more shortwave radiation in the surface ocean layer, which leads to Arctic surface warming. Here, we identified that in addition to the effect of the mean chlorophyll change, an interannual chlorophyll variability substantially influences the Arctic mean climate state, even though the mean chlorophyll remains the same. We found that two nonlinear rectifications of chlorophyll variability induced Arctic cooling. One was due to the effect of a nonlinear shortwave heating term, which was induced by the positive ice–phytoplankton covariability in the boreal summer. The other was due to a cooling effect by rectification of a nonlinear function of the shortwave absorption rate, which reduced the shortwave absorption rate by interannually varying chlorophyll. In the Coupled Model Intercomparison Project, earth system models that included biogeophysical feedback simulated a colder Arctic condition than models without a biogeophysical feedback. This result suggests a possible mechanism in understanding how chlorophyll variability interacts with the Arctic climate system and its impact on the Arctic mean climate state.
It has been shown that the interaction between marine phytoplankton and climate systems may intensify Arctic warming in the future via shortwave heating associated with increased spring chlorophyll bloom. However, the changes of chlorophyll variability and its impact on the Arctic future climate are uncomprehended. Lim et al. (Clim Dyn. https://doi.org/10.1007/s00382-018-4450-6, 2018a) (Part I) suggested that two nonlinear rectifications of chlorophyll variability play cooling role in present-day climate. In this study, we suggest that the decreased interannual chlorophyll variability may amplify Arctic surface warming (+ 10% in both regions) and sea ice melting (− 13% and − 10%) in Kara-Barents Seas and East Siberian-Chukchi Seas in boreal winter, respectively. Projections of earth system models show a future decrease in chlorophyll both mean concentration and interannual variability via sea ice melting and intensified surface-water stratification in summer. We found that suggested two nonlinear processes in Part I will be reduced by about 31% and 20% in the future, respectively, because the sea ice and chlorophyll variabilities, which control the amplitudes of nonlinear rectifications, are projected to decrease in the future climate. The Arctic warming is consequently enhanced by the weakening of the cooling effects of the nonlinear rectifications. Thus, this additional biological warming will contribute to future Arctic warming. This study suggests that effects of the mean chlorophyll and its variability should be considered to the sensitivity of Arctic warming via biogeophysical feedback processes in future projections using earth system models.
Climate modeling groups nowadays develop earth system models (ESMs) by incorporating biogeochemical processes in their climate models. The ESMs, however, often show substantial bias in simulated marine biogeochemistry which can potentially introduce an undesirable bias in physical ocean fields through biogeophysical interactions. This study examines how and how much the chlorophyll bias in a state-of-the-art ESM affects the mean and seasonal cycle of tropical Pacific sea-surface temperature (SST). The ESM used in the present study shows a sizeable positive bias in the simulated tropical chlorophyll. We found that the correction of the chlorophyll bias can reduce the ESM’s intrinsic cold SST mean bias in the equatorial Pacific. The biologically-induced cold SST bias is strongly affected by seasonally-dependent air–sea coupling strength. In addition, the correction of chlorophyll bias can improve the annual cycle of SST by up to 25%. This result suggests a possible modeling approach in understanding the two-way interactions between physical and chlorophyll biases by biogeophysical effects.
In order to examine the threshold of the volcanic forcing that leads to the El Niño-like warming, we analyze a millennium ERIK simulation (AD 1000–1850) forced by three external forcings including greenhouse gases, solar forcing and volcanic eruptions using the ECHO-G coupled climate model. It is found that there exists a threshold of the volcanic forcing above 15 W/m2 to lead the El Niño-like warming in the climate model. When the volcanic forcing is above this threshold forcing, then the intensity of the Inter-tropical Convergence Zone (ITCZ) is weakened and its position is shifted to the south. This might be associated with the processes of less evaporation in the subtropical cloudless region by a cooling due to the reduction of net surface shortwave radiation. Concurrently, a weakening of ITCZ is associated with a weakening of the trade winds and the subsequent Bjerknes feedback causes El Niño-like warming. Therefore, El Niño-like warming events can occur when volcanic eruption is above threshold forcing, implying that there exists a certain level of radiative forcing change which is capable of changing the state of tropical Pacific sea surface temperature. The last millennium simulation of Paleoclimate Modeling Intercomparison Project Phase 3 climate models also indicates that there may exist a threshold forcing to lead the El Niño-like warming, which has been also discussed in the present study.
Due to the dramatic increase in the global mean surface temperature (GMST) during the twentieth century, the climate science community has endeavored to determine which mechanisms are responsible for global warming. By analyzing a millennium simulation (the period of 1000–1990 ad) of a global climate model and global climate proxy network dataset, we estimate the contribution of solar and greenhouse gas forcings on the increase in GMST during the present warm period (1891–1990 ad). Linear regression analysis reveals that both solar and greenhouse gas forcing considerably explain the increase in global mean temperature during the present warm period, respectively, in the global climate model. Using the global climate proxy network dataset, on the other hand, statistical approach suggests that the contribution of greenhouse gas forcing is slightly larger than that of solar forcing to the increase in global mean temperature during the present warm period. Overall, our result indicates that the solar forcing as well as the anthropogenic greenhouse gas forcing plays an important role to increase the global mean temperature during the present warm period.
Postdoc in AOS program at Princeton University / NOAA-GFDL