Effect of current global warming trends on temperature-sensitive tri-trophic interactions

Observational constraint methods based on the relationship between the past global warming trend and projected warming across climate models were used to reduce uncertainties in projected warming by the Intergovernmental Panel on Climate Change. Internal climate variability in the eastern tropical Pacific associated with the so-called pattern effect weakens this relationship and has reduced the observed warming trend over recent decades. Here we show that regressing out this variability before applying the observed global mean warming trend as a constraint results in higher and narrower twenty-first century warming ranges than other methods. Whereas the Intergovernmental Panel on Climate Change assessed that warming is unlikely to exceed 2 °C under a low-emissions scenario, our results indicate that warming is likely to exceed 2 °C under the same scenario, and hence, limiting global warming to well below 2 °C will be harder than previously anticipated. However, the reduced uncertainties in these projections could benefit adaptation planning.

Both global warming and influenza trouble humans in varying ways, therefore it is important to study the trends in both global warming and evolution of influenza A virus, in particular, proteins from influenza A virus. Recently, we have conducted two studies along this line to determine the trends between global warming and polymerase acidic protein as well as matrix protein 2. Although these two studies reveal some interesting findings, many studies are still in need because at least there are ten different proteins in influenza A virus. In this study, we analyze the trends in global warming and evolution of polymerase basic protein 2 (PB2) from influenza A virus. The PB2 evolution from 1956 to 2008 was defined using the unpredictable portion of aminoacid pair. Then the trend in this evolution was compared with the trend in the global temperature, the temperature in north and south hemispheres, and the temperature in influenza A virus sampling site and species carrying influenza A virus. The results show the similar trends in global warming and in PB2 evolution, which are in good agreement with our previous studies in polymerase acidic protein and matrix protein 2 from influenza A virus.

Arctic warming trends and their uncertainties based on surface temperature reconstruction under different sea ice extent scenarios

The North Atlantic Oscillation (NAO) and the closely related Arctic Oscillation (AO) strongly affect Northern Hemisphere (NH) surface temperatures with patterns reported similar to the global warming trend. The NAO and AO were in a positive trend for much of the 1970s and 1980s with historic highs in the early 1990s, and it has been suggested that they contributed significantly to the global warming signal. The trends in standard indices of the AO, NAO, and NH average surface temperature for December–February, 1950–2004, and the associated patterns in surface temperature anomalies are examined. Also analyzed are factors previously identified as relating to the NAO, AO, and their positive trend: North Atlantic sea surface temperatures (SSTs), Indo–Pacific warm pool SSTs, stratospheric circulation, and Eurasian snow cover. Recently, the NAO and AO indices have been decreasing; when these data are included, the overall trends for the past 30 years are weak to nonexistent and are strongly dependent on the choice of start and end date. In clear distinction, the wintertime hemispheric warming trend has been vigorous and consistent throughout the entire period. When considered for the whole hemisphere, the NAO/AO patterns can also be distinguished from the trend pattern. Thus the December–February warming trend may be distinguished from the AO and NAO in terms of the strength, consistency, and pattern of the trend. These results are insensitive to choice of index or dataset. While the NAO and AO may contribute to hemispheric and regional warming for multiyear periods, these differences suggest that the large-scale features of the global warming trend over the last 30 years are unrelated to the AO and NAO. The related factors may also be clearly distinguished, with warm pool SSTs linked to the warming trend, while the others are linked to the NAO and AO.

Sea surface temperature (SST) is an important element in studying the global ocean-atmospheric system, as well as its simulation and projection in climate models. In this study, we evaluate the simulation skill of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating the climatological SST in the Asian Marginal Seas (AMS), known as the most rapidly warming region over the global ocean. The results show that the spatial patterns and seasonal variability of Asian Marginal Seas (AMS) climatological SST simulated by the CMIP6 models are generally in good agreement with the observations, but there are simulation biases in the values. In boreal winter, the simulated climatological SST tends to be overestimated in the Japan/East Sea and the East China Seas (ECSs) by up to 2°C, while being underestimated in the Sea of Okhotsk by up to 2°C. In boreal summer, the simulated climatological SSTs are overestimated in the Indonesian seas and western Arabian Sea, while being underestimated in the Sea of Okhotsk and the northern ECSs by 1.2–1.5 and 2°C, respectively. Furthermore, we calculate the projected sea surface warming trends in the AMS under different future scenarios in the CMIP6 models. The results show warming trends of 0.8–1.8, 1.7–3.4, and 3.8–6.5°C/century for the Shared Socio-Economic Pathway (SSP) of low- (global radiative forcing of 2.6 W/m² by the year 2100), medium- (global radiative forcing of 4.5 W/m² by 2100) and high-end (8.5 W/m² by 2100) pathways, respectively. In addition, the middle and high latitudes of the AMS are found to have faster warming trends than the low latitudes, with the most rapidly warming occurring in the Sea of Okhotsk, which is around 2 times larger than the global mean SST warming trend. The SST warming trends are relatively slow in the South China Sea and the Indonesian seas, roughly equal to the global mean SST warming trend.

Prerequisites for evolution: variation and selection in yellow autumn birch leaves

We have created a performance-based assessment of CMIP6 models for Europe that can be used to inform the sub-selection of models for this region. Our assessment covers criteria indicative of the ability of individual models to capture a range of large-scale processes that are important for the representation of present-day European climate. We use this study to provide examples of how this performance-based assessment may be applied to multi-model ensemble of CMIP6 models to a) filter the ensemble for performance against these climatological/ processed-based criteria and, b) create a smaller sub-set of models based on performance, that also maintains model diversity and the filtered projection range as far as possible. Filtering by excluding the least realistic models leads to higher sensitivity models remaining in the ensemble as an emergent consequence of the assessment. This results in both the 25th percentile and the median of the projected temperature range being shifted toward greater warming for the filtered set of models. We also weight the unfiltered ensemble against global trends. In contrast this shifts both the distribution of towards less warming. This highlights a tension for regional model selection in terms of selection based on regional climate processes versus the global mean warming trend.

We have created a performance-based assessment of CMIP6 models for Europe that can be used to inform the sub-selection of models for this region. Our assessment covers criteria indicative of the ability of individual models to capture a range of large-scale processes that are important for the representation of present-day European climate. We use this study to provide examples of how this performance-based assessment may be applied to multi-model ensemble of CMIP6 models to a) filter the ensemble for performance against these climatological/ processed-based criteria and, b) create a smaller sub-set of models based on performance, that also maintains model diversity and the filtered projection range as far as possible. Filtering by excluding the least realistic models leads to higher sensitivity models remaining in the ensemble as an emergent consequence of the assessment. This results in both the 25th percentile and the median of the projected temperature range being shifted toward greater warming for the filtered set of models. We also weight the unfiltered ensemble against global trends. In contrast this shifts both the distribution of towards less warming. This highlights a tension for regional model selection in terms of selection based on regional climate processes versus the global mean warming trend.

Abstract. We have created a performance-based assessment of CMIP6 models for Europe that can be used to inform the sub-selection of models for this region. Our assessment covers criteria indicative of the ability of individual models to capture a range of large-scale processes that are important for the representation of present-day European climate. We use this study to provide examples of how this performance-based assessment may be applied to a multi-model ensemble of CMIP6 models to (a) filter the ensemble for performance against these climatological and processed-based criteria and (b) create a smaller subset of models based on performance that also maintains model diversity and the filtered projection range as far as possible. Filtering by excluding the least-realistic models leads to higher-sensitivity models remaining in the ensemble as an emergent consequence of the assessment. This results in both the 25th percentile and the median of the projected temperature range being shifted towards greater warming for the filtered set of models. We also weight the unfiltered ensemble against global trends. In contrast, this shifts the distribution towards less warming. This highlights a tension for regional model selection in terms of selection based on regional climate processes versus the global mean warming trend.

We have created a performance-based assessment of CMIP6 models for Europe that can be used to inform the sub-selection of models for this region. Our assessment covers criteria indicative of the ability of individual models to capture a range of large-scale processes that are important for the representation of present-day European climate. We use this study to provide examples of how this performance-based assessment may be applied to multi-model ensemble of CMIP6 models to a) filter the ensemble for performance against these climatological/ processed-based criteria and, b) create a smaller sub-set of models based on performance, that also maintains model diversity and the filtered projection range as far as possible. Filtering by excluding the least realistic models leads to higher sensitivity models remaining in the ensemble as an emergent consequence of the assessment. This results in both the 25th percentile and the median of the projected temperature range being shifted toward greater warming for the filtered set of models. We also weight the unfiltered ensemble against global trends. In contrast this shifts both the distribution of towards less warming. This highlights a tension for regional model selection in terms of selection based on regional climate processes versus the global mean warming trend.

We have created a performance-based assessment of CMIP6 models for Europe that can be used to inform the sub-selection of models for this region. Our assessment covers criteria indicative of the ability of individual models to capture a range of large-scale processes that are important for the representation of present-day European climate. We use this study to provide examples of how this performance-based assessment may be applied to multi-model ensemble of CMIP6 models to a) filter the ensemble for performance against these climatological/ processed-based criteria and, b) create a smaller sub-set of models based on performance, that also maintains model diversity and the filtered projection range as far as possible. Filtering by excluding the least realistic models leads to higher sensitivity models remaining in the ensemble as an emergent consequence of the assessment. This results in both the 25th percentile and the median of the projected temperature range being shifted toward greater warming for the filtered set of models. We also weight the unfiltered ensemble against global trends. In contrast this shifts both the distribution of towards less warming. This highlights a tension for regional model selection in terms of selection based on regional climate processes versus the global mean warming trend.

We have created a performance-based assessment of CMIP6 models for Europe that can be used to inform the sub-selection of models for this region. Our assessment covers criteria indicative of the ability of individual models to capture a range of large-scale processes that are important for the representation of present-day European climate. We use this study to provide examples of how this performance-based assessment may be applied to multi-model ensemble of CMIP6 models to a) filter the ensemble for performance against these climatological/ processed-based criteria and, b) create a smaller sub-set of models based on performance, that also maintains model diversity and the filtered projection range as far as possible. Filtering by excluding the least realistic models leads to higher sensitivity models remaining in the ensemble as an emergent consequence of the assessment. This results in both the 25th percentile and the median of the projected temperature range being shifted toward greater warming for the filtered set of models. We also weight the unfiltered ensemble against global trends. In contrast this shifts both the distribution of towards less warming. This highlights a tension for regional model selection in terms of selection based on regional climate processes versus the global mean warming trend.

We have created a performance-based assessment of CMIP6 models for Europe that can be used to inform the sub-selection of models for this region. Our assessment covers criteria indicative of the ability of individual models to capture a range of large-scale processes that are important for the representation of present-day European climate. We use this study to provide examples of how this performance-based assessment may be applied to multi-model ensemble of CMIP6 models to a) filter the ensemble for performance against these climatological/ processed-based criteria and, b) create a smaller sub-set of models based on performance, that also maintains model diversity and the filtered projection range as far as possible. Filtering by excluding the least realistic models leads to higher sensitivity models remaining in the ensemble as an emergent consequence of the assessment. This results in both the 25th percentile and the median of the projected temperature range being shifted toward greater warming for the filtered set of models. We also weight the unfiltered ensemble against global trends. In contrast this shifts both the distribution of towards less warming. This highlights a tension for regional model selection in terms of selection based on regional climate processes versus the global mean warming trend.

Physically based observational constraint methods can effectively reduce uncertainty in global warming projections but have not been widely applied at regional scales. We first develop and apply multivariate linear regression models for constraining projections of surface air temperature averaged over subcontinental regions in the extratropical Northern Hemisphere, based on a set of potential constraints including climatological metrics derived from tropical and subtropical low-level cloud and global average past warming trend, as well as a set of regional climate metrics previously used in the literature. We evaluate the performance of the multivariate linear regression models based on cross-validated tests using output from phases 5 and 6 of the Coupled Model Intercomparison Projects (CMIP). We find that linear regression models using global-scale low-cloud metrics alone perform more robustly than linear regression models using the past global mean warming trend or regional climate metrics as constraints. These results, while favoring global constraints over the set of regional constraints considered, do not preclude the existence of even better regional constraints for particular regions. Through model-based cross-validation, the projections constrained using low-level cloud metrics exhibit more accurate best estimate projections, narrower uncertainty ranges, and more reliable uncertainty estimates in most Northern Hemisphere regions when compared with unconstrained projections. Application of the approach to climate projections based on both Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 using observed low-cloud metrics results in considerably narrower 5%–95% uncertainty ranges of twenty-first-century warming over subcontinental Northern Hemisphere land regions compared to unconstrained projections.

Past versions of global surface temperature (ST) datasets have been shown to have underestimated the recent warming trend over 1998–2012. This study uses a newly updated global land surface air temperature and a land and marine surface temperature dataset, referred to as China global land surface air temperature (C-LSAT) and China merged surface temperature (CMST), to estimate trends in the global mean ST (combining land surface air temperature and sea surface temperature anomalies) with the data uncertainties being taken into account. Comparing with existing datasets, the statistical significance of the global mean ST warming trend during the past century (1900–2017) remains unchanged, while the recent warming trend during the “hiatus” period (1998–012) increases obviously, which is statistically significant at 95% level when fitting uncertainty is considered as in previous studies (including IPCC AR5) and is significant at 90% level when both fitting and data uncertainties are considered. Our analysis shows that the global mean ST warming trends in this short period become closer among the newly developed global observational data (CMST), remotely sensed/Buoy network infilled datasets, and reanalysis data. Based on the new datasets, the warming trends of global mean land SAT as derived from C-LSAT 2.0 for the period of 1979–2019, 1951–2019, 1900–2019 and 1850–2019 were estimated to be 0.296, 0.219, 0.119 and 0.081 °C/decade, respectively. The warming trends of global mean ST as derived from CMST for the periods of 1998–2019, 1979–2019, 1951–2019 and 1900–2019 were estimated to be 0.195, 0.173, 0.145 and 0.091 °C/decade, respectively.