Ensemble Projections Research Articles

Constraining low-frequency variability in climate projections to predict climate on decadal to multi-decadal timescales – a poor man's initialized prediction system

Abstract. Near-term projections of climate change are subject to substantial uncertainty from internal climate variability. Here we present an approach to reduce this uncertainty by sub-selecting those ensemble members that more closely resemble observed patterns of ocean temperature variability immediately prior to a certain start date. This constraint aligns the observed and simulated variability phases and is conceptually similar to initialization in seasonal to decadal climate predictions. We apply this variability constraint to large multi-model projection ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6), consisting of more than 200 ensemble members, and evaluate the skill of the constrained ensemble in predicting the observed near-surface temperature, sea-level pressure, and precipitation on decadal to multi-decadal timescales. We find that the constrained projections show significant skill in predicting the climate of the following 10 to 20 years, and added value over the ensemble of unconstrained projections. For the first decade after applying the constraint, the global patterns of skill are very similar and can even outperform those of the multi-model ensemble mean of initialized decadal hindcasts from the CMIP6 Decadal Climate Prediction Project (DCPP). In particular for temperature, larger areas show added skill in the constrained projections compared to DCPP, mainly in the Pacific and some neighboring land regions. Temperature and sea-level pressure in several regions are predictable multiple decades ahead, and show significant added value over the unconstrained projections for forecasting the first 2 decades and the 20-year averages. We further demonstrate the suitability of regional constraints to attribute predictability to certain ocean regions. On the example of global average temperature changes, we confirm the role of Pacific variability in modulating the reduced rate of global warming in the early 2000s, and demonstrate the predictability of reduced global warming rates over the following 15 years based on the climate conditions leading up to 1998. Our results illustrate that constraining internal variability can significantly improve the accuracy of near-term climate change estimates for the next few decades.

Exploring spatio-temporal variation of drought characteristics and propagation under climate change using multi-model ensemble projections

Exploring spatio-temporal variation of drought characteristics and propagation under climate change using multi-model ensemble projections

Realizing certainty in an uncertain future climate: modeling suitable areas for conserving wild Citrus species under different change scenarios in India.

Citrus is an important horticultural crop of India and is often prone to diseases, particularly under increased temperature scenarios. For developing disease-resistant Citrus varieties, conservation of wild relatives is extremely important. However, our knowledge on temperature tolerance of these wild relatives of Citrus to varied climate change scenarios is extremely limited. Therefore, we determined the climatic niche of six wild relatives of cultivated Citrus species (C. indica Tanaka, C. karna Rafin., C. latipes (Swingle) Tanaka, C. macroptera Montrouz., C. medica L., and C. sinensis (L.) Osbeck.) and identified the geographical areas in India that would remain climatically stable in future through ecological niche modeling (ENM). Raster data on 19 bioclimatic variables with a resolution of 0.04° were used to generate niche models for each Citrus species that delineated their potential distribution areas. Future species distribution predictions for the year 2050 were made using the climate change scenarios from the most appropriate climate models, i.e., IPSL-CM5A-LR and NIMR-HADGEM2-AO with four Representative Concentration Pathways (RCPs). Ensemble of current and future projections was used to identify climatically stable areas for each species. Precipitation-related bioclimatic variables were the key climatic determinants for the modeled distribution pattern. The consensus of current and future projections suggests that most areas with stable climates for the species in the future would be available in the northeastern states of Arunachal Pradesh, Meghalaya, Mizoram, and Tripura. Efforts for in situ conservation and establishment of germplasm banks and citrus orchards may be encouraged in these identified areas.

Transformative changes in tree breeding for resilient forest restoration

Deciding how to establish woodland in forest restoration is not straightforward as different outcomes may be obtained from different establishment approaches, each with cost implications and degree of success limitations attached. Planning restoration requires knowledge of site conditions, including how sites are likely to respond under climate change. For objectives of production and high timber quality it is likely that ground preparation will be used, and planting with forest reproductive material (FRM) of known traits, such as: high survival and growth in establishment, drought tolerance adequate for climate projections, good resistance to pests and pathogens. For objectives associated with biodiversity, carbon sequestration, water supply protection, soil protection, natural regeneration could be a less costly solution with a limited amount of assisted translocation of selected FRM to improve resilience. If objectives are for rewilding forest areas, a degree of natural colonisation perhaps with translocation of some FRM could be a solution. Ignoring site conditions and suitability of available sources of FRM for forest restoration is likely to provide unexpected results with a mix of open ground, scrub and scattered trees resulting from climate, herbivore, and browsing impacts. The recent B4EST EU Horizon 2020 project examined progress in novel rapid approaches for testing the quality of FRM from existing genetic trials. Here we review the work of B4EST to show the opportunities from transformative tree breeding in forest restoration schemes, including: new climate projection ensembles at high temporal and spatial resolution to develop norms of reaction and transfer models with genetic components; multi-environment genotype-phenotype associations and multi-locus genotype-environment associations in identifying drivers of local adaptation; techniques for genomic selection using single nucleotide polymorphism (SNP) arrays to derive functional traits from polygenic associations; work on seed orchard site and climate specific FRM and zones for deployment; and work on some of the forest ecosystem service benefits derived at a landscape scale. We conclude that tree-breeding will provide robust forest restoration for planting, and rewilding (assisted natural regeneration), and if not “ignoring” but instead assisting natural colonisation processes – tree breeding may improve long-term forest resilience under environmental change.

Negative Storm Surges in the Elbe Estuary—Large-Scale Meteorological Conditions and Future Climate Change

Negative storm surges in the Elbe estuary can affect shipping as well as shoreline infrastructure. The significant reduction of water level caused by strong offshore winds can lead to extreme low water events, which endanger waterfront structures. The current study analyses the large-scale meteorological conditions inducing such situations and possible future changes due to climate change. The analysis is based on tide gauge data from Cuxhaven, atmospheric reanalysis data and an objective weather classification approach. It is found that south-easterly wind directions in combination with strong gales favour extreme low water events at Cuxhaven. Furthermore, the analysis of a single model large ensemble of climate projections shows a significant decrease in the frequency of such conditions for the far future (2071–2100). Regarding future global mean sea level rise the simulation results of a sensitivity study indicate that water levels during such extreme events approximately follow the development of the mean sea level rise. Therefore, our study suggests that both meteorological conditions and mean sea levels in a warmer future climate will be less favourable for the occurrence of extreme low water events in the Elbe estuary.

Dipole response of early‐summer rainfall in eastern China to 1.5 and 2.0°C global warming

AbstractThe 2015 Paris Agreement set the goal of pursuing efforts to keep global warming to 1.5°C above the pre‐industrial level. It remains unclear how the early‐summer rainfall in China responds to moderate warming. Here, we investigate the changes in June rainfall in eastern China by 1.5 and 2.0°C warming using the ensemble projection from Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) dataset. Results reveal a dipole response in the June rainfall with the significant increase in southeastern China but a slight decrease in northeastern China at 1.5 and 2.0°C warming compared to the present day. The moisture budget analysis indicates that the dipole rainfall response is caused predominantly by the dynamic response in the anomalous circulation and in turn moisture transport. Compared to the present day, the upper‐tropospheric temperature is significantly enhanced in the tropics but decreased in middle latitudes at the 1.5 and 2.0°C warming. The enhanced poleward temperature gradient strengthens zonal wind on the south of the East Asian westerly jet, leading to a southward displacement of jet. As a result, an anomalous upward motion is reinforced over the north of 30°N with an anomalous downward motion over the south of 30°N, leading to a dipole rainfall anomaly in eastern China. In addition, the warming also induces a thermodynamic response with increased moisture and in turn rainfall across eastern China, further enhancing rainfall in southeastern China, but weakening the decreased rainfall in northeastern China.

Future changes in extreme storm surge based on a maximum potential storm surge model for East Asia

ABSTRACT We analyzed tropical cyclones (TC) based on the theory of Maximum Potential Intensity (MPI) and Maximum Potential Surge (MPS) for a long-term assessment of extreme TC intensity and storm surge heights. We investigated future changes in the MPI fields and MPS for different global warming levels based on 150-year continuous scenario projections (HighResMIP) and large ensemble climate projections (d4PDF/d2PDF). Focusing on the Western North Pacific Ocean (WNP), we analyzed future changes in the MPI and found that it reached a maximum in the latitudinal range of 30–40°N in September. We also analyzed future changes in the MPS in major bays of East Asia and along the Pacific coast of Japan. Future changes in the MPS were projected, and it was confirmed that changes in the MPS are larger in bays where large storm surge events have occurred in the past.

En-GARD: A Statistical Downscaling Framework to Produce and Test Large Ensembles of Climate Projections

Abstract Statistical processing of numerical model output has been a part of both weather forecasting and climate applications for decades. Statistical techniques are used to correct systematic biases in atmospheric model outputs and to represent local effects that are unresolved by the model, referred to as downscaling. Many downscaling techniques have been developed, and it has been difficult to systematically explore the implications of the individual decisions made in the development of downscaling methods. Here we describe a unified framework that enables the user to evaluate multiple decisions made in the methods used to statistically postprocess output from weather and climate models. The Ensemble Generalized Analog Regression Downscaling (En-GARD) method enables the user to select any number of input variables, predictors, mathematical transformations, and combinations for use in parametric or nonparametric downscaling approaches. En-GARD enables explicitly predicting both the probability of event occurrence and the event magnitude. Outputs from En-GARD include errors in model fit, enabling the production of an ensemble of projections through sampling of the probability distributions of each climate variable. We apply En-GARD to regional climate model simulations to evaluate the relative importance of different downscaling method choices on simulations of the current and future climate. We show that choice of predictor variables is the most important decision affecting downscaled future climate outputs, while having little impact on the fidelity of downscaled outcomes for current climate. We also show that weak statistical relationships prevent such approaches from predicting large changes in extreme events on a daily time scale.

Global Increases in Lethal Compound Heat Stress: Hydrological Drought Hazards Under Climate Change

AbstractPrevious studies seldom consider humidity when examining heat‐related extremes, and none have explored the effects of humidity on concurrent extremes of high heat stress and low river streamflow. Here, we present the first global picture of projected changes in compound lethal heat stress (Th)‐drought hazards (CHD) across 11,637 catchments. Our observational datasets show that atmospheric conditions (e.g., energy and vapor flux) play an important role in constraining the heat extremes, and that Th (32% ± 11%) yields a higher coincidence rate of global CHD than wet‐bulb temperature (28% ± 11%), driven by lower relative humidity (RH) and thus air dryness in Th extremes. Our large model ensemble projects a 10‐fold intensification of bivariate CHD risks by 2071–2100, mainly driven by increases in heat extremes. Future declines in RH, wind, snow, and precipitation in many regions are likely to exacerbate such water and weather‐related hazards (e.g., drought and CHD).

Future changes and seasonal variability of the directional wave spectra in the Mediterranean Sea for the 21st century

A state-of-the-art regional assessment of future directional wave spectra in the Mediterranean Sea and the projected changes with respect to hindcast is presented. A multi-model EURO-CORDEX regional ensemble of bias-adjusted wave climate projections in eleven locations of the Mediterranean are used for the assessment of future seasonal changes in the directional wave spectra under the high-emission scenario RCP8.5. This analysis allows us to identify climate change effects on the spectral energy of the swell and wind-sea systems and their seasonal variability which cannot be captured with the standard integrated wave parameters, such as significant wave height and mean wave direction. The results show an overall robust decrease in the predominant wave systems, resulting in a likely decrease in the significant wave height that is in agreement with previous studies. However, the results depict a robust increase in other less energetic frequencies and directions leading to a projected behavioral change from unimodal to bimodal/multimodal wave climate in many locations which has strong repercussions on the vulnerability of coastal assets and ports operability.

Evaluation of Present-Day CMIP6 Model Simulations of Extreme Precipitation and Temperature over the Australian Continent

Australia experiences a variety of climate extremes that result in loss of life and economic and environmental damage. This paper provides a first evaluation of the performance of state-of-the-art Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCMs) in simulating climate extremes over Australia. Here, we evaluate how well 37 individual CMIP6 GCMs simulate the spatiotemporal patterns of 12 climate extremes over Australia by comparing the GCMs against gridded observations (Australian Gridded Climate Dataset). This evaluation is crucial for informing, interpreting, and constructing multimodel ensemble future projections of climate extremes over Australia, climate-resilience planning, and GCM selection while conducting exercises like dynamical downscaling via GCMs. We find that temperature extremes (maximum-maximum temperature -TXx, number of summer days -SU, and number of days when maximum temperature is greater than 35 °C -Txge35) are reasonably well-simulated in comparison to precipitation extremes. However, GCMs tend to overestimate (underestimate) minimum (maximum) temperature extremes. GCMs also typically struggle to capture both extremely dry (consecutive dry days -CDD) and wet (99th percentile of precipitation -R99p) precipitation extremes, thus highlighting the underlying uncertainty of GCMs in capturing regional drought and flood conditions. Typically for both precipitation and temperature extremes, UKESM1-0-LL, FGOALS-g3, and GCMs from Met office Hadley Centre (HadGEM3-GC31-MM and HadGEM3-GC31-LL) and NOAA (GFDL-ESM4 and GFDL-CM4) consistently tend to show good performance. Our results also show that GCMs from the same modelling group and GCMs sharing key modelling components tend to have similar biases and thus are not highly independent.

Downscaling multi-model climate projection ensembles with deep learning (DeepESD): contribution to CORDEX EUR-44

Abstract. Deep learning (DL) has recently emerged as an innovative tool to downscale climate variables from large-scale atmospheric fields under the perfect-prognosis (PP) approach. Different convolutional neural networks (CNNs) have been applied under present-day conditions with promising results, but little is known about their suitability for extrapolating future climate change conditions. Here, we analyze this problem from a multi-model perspective, developing and evaluating an ensemble of CNN-based downscaled projections (hereafter DeepESD) for temperature and precipitation over the European EUR-44i (0.5∘) domain, based on eight global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). To our knowledge, this is the first time that CNNs have been used to produce downscaled multi-model ensembles based on the perfect-prognosis approach, allowing us to quantify inter-model uncertainty in climate change signals. The results are compared with those corresponding to an EUR-44 ensemble of regional climate models (RCMs) showing that DeepESD reduces distributional biases in the historical period. Moreover, the resulting climate change signals are broadly comparable to those obtained with the RCMs, with similar spatial structures. As for the uncertainty of the climate change signal (measured on the basis of inter-model spread), DeepESD preserves the uncertainty for temperature and results in a reduced uncertainty for precipitation. To facilitate further studies of this downscaling approach, we follow FAIR principles and make publicly available the code (a Jupyter notebook) and the DeepESD dataset. In particular, DeepESD is published at the Earth System Grid Federation (ESGF), as the first continental-wide PP dataset contributing to CORDEX (EUR-44).

Assessing future runoff changes with different potential evapotranspiration inputs based on multi-model ensemble of CMIP5 projections

• Xinanjiang model performed well in runoff simulation in North Johnstone catchment. • Runoff projected with different ETp inputs showed similar changes in the future. • Runoff in spring and winter would decrease in the future. • GCMs and RCPs were major factors resulting in uncertainty in runoff projections. Runoff projection under future climate scenarios has been widely studied to investigate the impacts of climate change on regional water availability. However, uncertainty in runoff projection due to different ETp inputs has not been fully assessed. This study firstly adopted the physically-based Penman model, temperature-based Hargreaves model, and radiation-based Abtew, Jensen-Haise, and modified Makkink models to drive Xinanjiang (XAJ) model, thus investigating the influence of different potential evapotranspiration (ETp) inputs on runoff simulation. Then, we used the validated XAJ model to project runoff in North Johnstone catchment, northeast Australia. Lastly, we quantified the uncertainty caused by 34 global climate models (GCMs), different representative concentrative pathway (RCP) scenarios (RCP4.5 & RCP8.5), and different ETp models with the technique of three-way analysis of variance (ANOVA). We found that XAJ model performed well (R 2 ≥ 0.88, NSE ≥ 0.86) and showed low sensitivity to different ETp inputs in runoff simulation and projection. Under future climate scenarios, spring and winter runoff had a large decrease, which was mainly caused by the decrease in rainfall. The mean decreases in spring and winter runoff were 14.6% – 20.1% and 10.3% – 15.2% respectively by 2090s under RCP8.5. GCMs (50.9% – 67.4%) and their interaction with RCPs (35.4% – 46.6%) were the dominant factors resulting in uncertainty in runoff projection. Our study not only advanced the understanding of the impacts of different ETp inputs on runoff projection but also offered insights on the understanding of the roles different factors played in the uncertainty in runoff projection. We expect such knowledge can provide a way forward to narrow down the uncertainty in runoff projection, thus providing more robust information for policy makers in water management.

Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050

Context. Sites for next-generation telescopes are chosen decades before the first light of a telescope. Site selection is usually based on recent measurements over a period that is too short to account for long-term changes in observing conditions such as those arising from anthropogenic climate change. For astronomical facilities with a typical lifetime of 30 yr, it is therefore essential to be aware of climate evolution to optimise observing time. Aims. In this study, we analyse trends in astronomical observing conditions for eight sites. Most sites either already host telescopes that provide in situ measurements of weather parameters or are candidates for hosting next-generation telescopes. For a fine representation of orography, we use the highest resolution global climate model (GCM) ensemble available provided by the high-resolution model intercomparison project and developed as part of the European Union Horizon 2020 PRIMAVERA project. Methods. We evaluate atmosphere-only and coupled PRIMAVERA GCM historical simulations against in situ measurements and the fifth generation atmospheric reanalysis (ERA5) of the European centre for medium-range weather forecasts for the period 1979–2014. The projections of changes in current site conditions are then analysed for the period 2015–2050 using PRIMAVERA future climate simulations. Results. Over most sites, we find that PRIMAVERA GCMs show a good agreement in temperature, specific humidity, and precipitable water vapour compared to in situ observations and ERA5. The ability of PRIMAVERA to simulate those variables increases confidence in their projections. For those variables, the model ensemble projects an increasing trend for all sites, which will result in progressively poorer astronomical observing conditions compared to current conditions. On the other hand, no significant trends are projected for relative humidity, cloud cover, or astronomical seeing and PRIMAVERA does not simulate these variables well compared to observations and reanalyses. Therefore, there is little confidence in these projections. Conclusions. Our results show that climate change will negatively impact the quality of astronomical observations and is likely to increase time lost due to bad site conditions. We stress that it is essential for astronomers to include long-term climate projections in their process for site selection and monitoring. We show that high-resolution GCMs can be used to analyse the effect of climate change on site characteristics of next-generation telescopes.

A New Model for Isolating the Marine Heatwave Changes under Warming Scenarios

Abstract Marine heatwaves (MHWs) exert devastating impacts on ecosystems. Understanding their responses to anthropogenic forcing has attracted rapidly growing scientific interest. Given the disparate adaptation capacity and mobility among marine species, it is crucial to disentangle changes of MHWs related to the rising mean temperature from those to the changing temperature variability. It has been suggested that the latter’s effects could be isolated by replacing a fixed baseline with a moving one for calculating the climatological mean and percentile metrics in MHW analysis. In this study, the effects of a moving baseline on MHW statistics (annual MHW days and cumulative intensity) changes in a warming climate are systematically evaluated based on simulations from simple stochastic climate models and a set of coupled general circulation models in the Community Earth System Model Large Ensemble project. On the one hand, a moving baseline does not necessarily remove the influences of rising mean temperature on MHW changes and will artificially cause negative trends in MHW statistics when the ocean exhibits an accelerated warming rate as in the RCP8.5 scenario. On the other hand, it always underestimates the effects of changing temperature variability on MHW changes. We propose a new model that adopts a “partial” moving baseline combined with a local linear detrending to isolate MHW changes caused by changing temperature variability. Significance Statement A new model is proposed to isolate marine heatwave changes caused by changing temperature variability from those by rising mean temperature under greenhouse warming.

Improved Monthly and Seasonal Multi-Model Ensemble Precipitation Forecasts in Southwest Asia Using Machine Learning Algorithms

Southwest Asia has different climate types including arid, semiarid, Mediterranean, and temperate regions. Due to the complex interactions among components of the Earth system, forecasting precipitation is a difficult task in such large regions. The aim of this paper is to propose a learning approach, based on artificial neural network (ANN) and random forest (RF) algorithms for post-processing the output of forecasting models, in order to provide a multi-model ensemble forecasting of monthly precipitation in southwest Asia. For this purpose, four forecasting models, including GEM-NEMO, NASA-GEOSS2S, CanCM4i, and COLA-RSMAS-CCSM4, included in the North American multi-model ensemble (NMME) project, are considered for the ensemble algorithms. Since each model has nine different lead times, a total of 108 different ANN and RF models are trained for each month of the year. To train the proposed ANN an RF models, the ERA5 reanalysis dataset is employed. To compare the performance of the proposed algorithms, four performance evaluation criteria are calculated for each model. The results indicate that the performance of the ANN and RF post-processing is better than that of the individual NMME models. Moreover, RF outperformed ANN for all lead times and months of the year.

Assessment of Tropical Atlantic ocean variability and associated rainfall sensibility due to atmospheric ozone changes

Considering the impacts of atmospheric ozone changes on climate, its effects on Tropical Atlantic Variability (TAV) since 1955 are investigated. Two ensembles of simulations from the Community Earth System Model Large Ensemble Project are examined. One of them (varO3) considers historic tropospheric and stratospheric ozone levels while the other (fixO3) keeps ozone fixed at the 1955 levels. Results for varO3 reveal a warming in the northern Tropical Atlantic (TA) when compared to fixO3, which is more pronounced in boreal spring, and a cooling of the southern TA in early summer. A dipole-like pattern was also observed in the mean zonal wind stress field, with a weakening of the trade winds in northern TA and strengthening in southern TA, although the strongest difference is observed in the southern westerlies, which are intensified. As for the impact on the main rainfall regions, results show differences between the experiments in the depiction of the Atlantic Ocean convergence zones. The fixed ozone experiment shifts the ITCZ position to the south, which leads to an increase in precipitation over most of Northeast Brazil and western South Africa, in addition to simulating lower precipitation in the South Atlantic Convergence Zone region. However, although there are differences in SST and precipitation between varO3 and fixO3, the ozone influence on TAV is not significant.

Climate change projection over Mainland Southeast Asia and the Lancang‐Mekong River basin based on a set of RegCM4 simulations

AbstractWe investigate the projected climate change over Mainland Southeast Asia (also known as the Indochina Peninsula) and the Lancang‐Mekong River basin, a region with complex topography and unique weather and climate systems, but limited availability of published high‐resolution regional climate model (RCM) studies. The study is based on an unprecedented ensemble of 21st century projections with the RegCM4 RCM driven by five different general circulation models (GCMs) at a grid spacing of 25 km under the representative concentration pathways RCP4.5 and RCP8.5. We focus on mean temperature and precipitation in the dry season November–March (NDJFM), the wet season May–September (MJJAS), and the whole year. Intercomparison between the RegCM4 simulations with the driving GCMs is provided to illustrate the added value of the RegCM4 experiments. RegCM4 reproduces greater and more realistic spatial detail of the present day temperature and precipitation distribution compared to the driving GCMs, but some biases are found, such as an overestimation of precipitation over high topography regions. The spatial pattern of biases shows some consistencies across the GCMs and, for NDJFM the RegCM4, although weak correlation is found between the GCM and nested RegCM4 biases. A generally lower warming is projected in the future by the RegCM4 in different seasons and the whole year. For precipitation, while prevailing increases are found in the GCM projections, large areas of decrease occur in the RegCM ones, in particular during the wet season, possibly due to the more detailed topographical representation. The change patterns of precipitation show consistencies across the GCMs and the RegCM4, especially in MJJAS. The projected changes of extreme indices indicate a general decrease/increase of extreme cold/warm events. Drought events are projected to be more frequent over the southwestern, while a general increase of heavy rain events prevails over most parts of the region.

Climate refugia on the Great Barrier Reef fail when global warming exceeds 3°C.

Increases in the magnitude, frequency, and duration of warm seawater temperatures are causing mass coral mortality events across the globe. Although, even during the most extensive bleaching events, some reefs escape exposure to severe stress, constituting potential refugia. Here, we identify present‐day climate refugia on the Great Barrier Reef (GBR) and project their persistence into the future. To do this, we apply semi‐dynamic downscaling to an ensemble of climate projections released for the IPCC's recent sixth Assessment Report. We find that GBR locations experiencing the least thermal stress over the past 20 years have done so because of their oceanographic circumstance, which implies that longer‐term persistence of climate refugia is feasible. Specifically, tidal and wind mixing of warm water away from the sea surface appears to provide relief from warming. However, on average this relative advantage only persists until global warming exceeds ~3°C.

Response of moist and dry processes in atmospheric blocking to climate change

Weather extremes are often associated with atmospheric blocking, but how the underlying physical processes leading to blocking respond to climate change is not yet fully understood. Here we track blocks as upper-level negative potential vorticity (PV) anomalies and apply a Lagrangian analysis to 100 years of present-day (∼2000) and future (∼2100, under the RCP8.5 scenario) climate simulations restarted from the Community Earth System Model–Large Ensemble Project runs (CESM-LENS) to identify different physical processes and quantify how their relative importance changes in a warmer and more humid climate. The trajectories reveal two contrasting airstreams that both contribute to the formation and maintenance of blocking: latent heating in strongly ascending airstreams (moist processes) and quasi-adiabatic flow near the tropopause with weak radiative cooling (dry processes). Both are reproduced remarkably well when compared against ERA-Interim reanalysis, and their relative importance varies regionally and seasonally. The response of blocks to climate change is complex and differs regionally, with a general increase in the importance of moist processes due to stronger latent heating (+1 K in the median over the Northern Hemisphere) and a larger fraction (+15%) of strongly heated warm conveyor belt air masses, most pronounced over the storm tracks. Future blocks become larger (+7%) and their negative PV anomaly slightly intensifies (+0.8%). Using a Theil–Sen regression model, we propose that the increase in size and intensity is related to the increase in latent heating, resulting in stronger cross-isentropic transport of air with low PV into the blocking anticyclones. Our findings provide evidence that moist processes become more important for the large-scale atmospheric circulation in the midlatitudes, with the potential for larger and more intense blocks.