World Climate Research Programme Research Articles

Global Precipitation Experiment - A New World Climate Research Programme Lighthouse Activity

Abstract The future state of the global water cycle and prediction of freshwater availability for humans around the world remain among the challenges of climate research and are relevant to several United Nations Sustainable Development Goals. The Global Precipitation EXperiment (GPEX) takes on the challenge of improving the prediction of precipitation quantity, phase, timing and intensity, characteristics that are products of a complex integrated system. It will achieve this by leveraging existing World Climate Research Programme (WCRP) activities and community capabilities in satellite, surface-based, and airborne observations, modeling and experimental research, and by conducting new and focused activities. It was launched in October 2023 as a WCRP Lighthouse Activity. Here we present an overview of the GPEX Science Plan that articulates the primary science questions related to precipitation measurements, process understanding, model performance and improvements, and plans for capacity development. The central phase of GPEX is the WCRP Years of Precipitation for 2-3 years with coordinated global field campaigns focusing on different storm types (atmospheric rivers, mesoscale convective systems, monsoons, and tropical cyclones, among others) over different regions and seasons. Activities are planned over the three phases (before, during, and after the Years of Precipitation) spanning a decade. These include gridded data evaluation and development, advanced modeling, enhanced understanding of processes critical to precipitation, multi-scale prediction of precipitation events across scales, and capacity development. These activities will be further developed as part of the GPEX Implementation Plan.

Model Mean State Sea Ice Thickness Reflects Dynamic Effect Biases: A Process Based Evaluation

AbstractGlobal climate models account for sea ice thickness by summing thermodynamic processes that affect thickness through phase change and dynamic processes that affect thicknesses through relative motion. Comparison of these individual processes with observations is essential for model interpretation and development. We utilized observational estimates of basal thermodynamic growth, overall thickness changes and their residual difference (including dynamics) to evaluate these processes in the National Center for Atmospheric Research (NCAR) Community Earth System Model 2 (CESM2) submission to the World Climate Research Program (WCRP) Ocean Model Comparison Project Phase 2 (OMIP2) and Pan‐Arctic Ice–Ocean Modeling and Assimilation System (PIOMAS). Both models exhibit a similar pattern of higher basal thermodynamic growth and lower residual effects and wintertime thickness in the central Arctic than observational estimates for 2010–2018, and vice versa in the peripheral seas. Correcting residual effect biases would ameliorate the biases in both mean thickness and basal thermodynamic growth.

Assessment of extreme rainfall events over the Indian subcontinent during the historical and future projection periods based on CMIP6 simulations

AbstractThe impact of climatic change on the summer monsoon season is studied to understand the rainfall pattern towards the end of the century utilizing the Coupled Model Intercomparison Project, Phase 6 (CMIP6) released by the World Climate Research Programme (WCRP). The analysis of model simulations from CMIP6 was carried out using 64 years of the historical period (1951–2014) and future projections till the end of the century (2015–2100). The models are compared with observed daily rainfall data from the APHRODITE (Asian Precipitation‐Highly‐Resolved Observational Data Integration Towards Evaluation). The analysis revealed that most of the models show an overestimation in the annual cycle of rainfall in the historical period; however, a few of them underestimate the values. The majority of them capture the onset signal of the summer monsoon in early June, along with a good seasonality in the daily rainfall climatology. The simulations that are coherent with the observational data sets are selected on the basis of the Taylor diagram for future projections in four scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). The projections of the aforementioned scenarios are taken from the model outputs of EC‐Earth3‐Veg‐LR, INM‐CM4‐8, INM‐CM5‐0, MIROC‐ES2L and MPI‐ESM1‐2‐HR. The selected models exhibit far greater agreement among the 30 models when it comes to the features of rainfall during the summer monsoon. We have given more emphasis on summer monsoon rainfall in the historical and future projection periods since the trends are becoming more chaotic, as reported in observational studies. Over the Indian subcontinent, all of the chosen scenarios show an increased frequency of intense rainfall events with varying decadal and multidecadal features. Central India and west coastal belts are showing positive trends in extreme rainfall events towards the end of the century. At the turn of the century, the southern peninsular region experienced a decline in monsoon precipitation, whereas central India experienced an increase. The severity of rainfall variations during the monsoon season and trends in extremes are increasing as we move from the low‐emission scenario to the high‐emission scenario.

High-resolution land use and land cover dataset for regional climate modelling: historical and future changes in Europe

Abstract. Anthropogenic land use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently being extensively investigated within the World Climate Research Program (WCRP) Coordinated Downscaling Experiment (CORDEX) Flagship Pilot Study (FPS) Land Use and Climate Across Scales (LUCAS) using an ensemble of different regional climate models (RCMs) coupled with diverse land surface models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM–LSM simulations. To account for these needs, we generated the LUCAS Land Use and land Cover change (LUC) dataset version 1.1 at 0.1∘ resolution for Europe with annual LULC maps from 1950 to 2100 (https://doi.org/10.26050/WDCC/LUC_hist_EU_v1.1, Hoffmann et al., 2022b, https://doi.org/10.26050/WDCC/LUC_future_EU_v1.1, Hoffmann et al., 2022a), which is tailored to use in state-of-the-art RCMs. The plant functional type (PFT) distribution for the year 2015 (i.e. the Modelling human LAND surface Modifications and its feedbacks on local and regional cliMATE – LANDMATE – PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details on the conversion method, cross-walking procedure, and evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (2022b). Subsequently, we applied the land use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25∘ resolution as input for Coupled Modelling Intercomparison Project Phase 6 (CMIP6) experiments, to derive LULC distributions at high spatial resolution and at annual time steps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a land use translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different shared socioeconomic pathway (SSP) and representative concentration pathway (RCP) combinations used in the framework of CMIP6. The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions worldwide.

Predicting climate-change induced heat-related illness risk in Grand Canyon National Park visitors.

The climate crisis is the greatest public health threat of the 21st century. Excessive heat is responsible for more deaths than any other extreme weather event, and the frequency, intensity, and duration of extreme heat events are increasing globally due to climate change. Exposure to excessive heat can result in heat related illnesses (HRIs) and long-term poor health outcomes. Physical exertion, sudden exposure to excessive heat, and the lack of physical or behavioral adaptation resources are all associated with greater HRI risk, which is expected to increase for visitors to Grand Canyon National Park (GCNP) and other public lands as climate change worsens. Our objectives were to understand 1) the relationship between weather and HRI in GCNP visitors, 2) how future HRI rates may change, and 3) how land management agencies can update risk mitigation strategies to match changing risk and better manage an increased HRI burden. We utilized previously published data on HRI in GCNP visitors, and records of daily visitation, temperatures, and maximum and minimum daily humidity from the same study period to develop a model estimate for HRI risk. We then used future climate projections from the World Climate Research Programme's Coupled Model Intercomparison Project phase 5 multi-model dataset to model future HRI risk under different climate scenarios. The incidence of HRI was significantly associated with maximum daily temperature and minimum relative humidity, and was more common in the shoulder season months. We estimated that HRI will increase 29%-137% over 2004-2009 levels through 2100, assuming no change in visitation. Climate change will continue to increase HRI risk for GCNP visitors and poses risks to public land managers' mission to provide for safe recreation experiences for the benefit of this and future generations in places like GCNP. Excessive risk during the shoulder season months presents an opportunity to increase preventative search and rescue and education efforts to mitigate increased risk.

Various ways of using empirical orthogonal functions for climate model evaluation

Abstract. We present a framework for evaluating multi-model ensembles based on common empirical orthogonal functions (common EOFs) that emphasize salient features connected to spatio-temporal covariance structures embedded in large climate data volumes. This framework enables the extraction of the most pronounced spatial patterns of coherent variability within the joint dataset and provides a set of weights for each model in terms of the principal components which refer to exactly the same set of spatial patterns of covariance. In other words, common EOFs provide a means for extracting information from large volumes of data. Moreover, they can provide an objective basis for evaluation that can be used to accentuate ensembles more than traditional methods for evaluation, which tend to focus on individual models. Our demonstration of the capability of common EOFs reveals a statistically significant improvement of the sixth generation of the World Climate Research Programme (WCRP) Climate Model Intercomparison Project (CMIP6) simulations in comparison to the previous generation (CMIP5) in terms of their ability to reproduce the mean seasonal cycle in air surface temperature, precipitation, and mean sea level pressure over the Nordic countries. The leading common EOF principal component for annually or seasonally aggregated temperature, precipitation, and pressure statistics suggests that their simulated interannual variability is generally consistent with that seen in the ERA5 reanalysis. We also demonstrate how common EOFs can be used to analyse whether CMIP ensembles reproduce the observed historical trends over the historical period 1959–2021, and the results suggest that the trend statistics provided by both CMIP5 RCP4.5 and CMIP6 SSP245 are consistent with observed trends. An interesting finding is also that the leading common EOF principal component for annually or seasonally aggregated statistics seems to be approximately normally distributed, which is useful information about the multi-model ensemble data.

The Path to Climate Sustainability: A Review of IPCC 2022

Working Group II of the IPCC Sixth Assessment Report focuses on climate change impacts, adaptation, and vulnerability in the context of sustainable development. This IPCC evaluates climate model simulations from CMIP5 and CMIP6 of the World Climate Research Programme. Depending on global warming levels after 2040, climate change will threaten natural and human systems. Mid- and long-term consequences are several times higher for 127 identified major hazards. Climate change is having increasingly dire, interconnected, and often irreversible effects on ecosystems, biodiversity, and human systems, and risk provides a framework for comprehending these effects and for devising strategies to prevent unfavourable outcomes for present and future generations. As climate change implications and actions are enacted, more is known about how they affect justice and socioeconomic advancement. AR6 emphasises change and quick climate action to achieve such goals.

Promoting involvement of early-career scientists from the Asia-Pacific region in regional integrated and sustainable development through active participation and networking

Climate change is affecting global ecosystems, natural resources, and human well-being. The near- and long-term future sustainable development of society requires robust climate change information at regional scales. To contribute to the purpose mentioned above, WCRP CORDEX (the World Climate Research Programme’s Coordinated Regional Climate Downscaling Experiment) initialised a collaboration with the APN (Asia-Pacific Network for Global Change Research), as the two programmes share common goals in advocating climate science as well as transferring climate knowledge for effective management. This APN project, entitled “Promoting Involvement of Early Career Scientists from the Asia-Pacific Region in Regional Integrated and Sustainable Development through Active Participation and Networking”, was a result of this collaboration. Specifically, the project was aimed at supporting early-career scientists from the Asia-Pacific region to attend an international science conference on regional climate science (ICRC-CORDEX 2019) and facilitate them in international partnership-building. It also contributed to enhancing communication and cooperation amongst regional climate research teams within and beyond the Asia-Pacific region. As one of the most important activities of the conference, the project supported an event for early-career scientists. The completion of the project consolidated global collaboration between the climate research community and that of adaptation-impact studies, as well as facilitated interaction with end-users. It was also a successful showcase of the scientific strategies of APN and CORDEX.

Explaining and Predicting Earth System Change: A World Climate Research Programme Call to Action

Abstract The World Climate Research Programme (WCRP) envisions a world “that uses sound, relevant, and timely climate science to ensure a more resilient present and sustainable future for humankind.” This bold vision requires the climate science community to provide actionable scientific information that meets the evolving needs of societies all over the world. To realize its vision, WCRP has created five Lighthouse Activities to generate international commitment and support to tackle some of the most pressing challenges in climate science today. The overarching goal of the Lighthouse Activity on Explaining and Predicting Earth System Change is to develop an integrated capability to understand, attribute, and predict annual to decadal changes in the Earth system, including capabilities for early warning of potential high impact changes and events. This article provides an overview of both the scientific challenges that must be addressed, and the research and other activities required to achieve this goal. The work is organized in three thematic areas: (i) monitoring and modeling Earth system change; (ii) integrated attribution, prediction, and projection; and (iii) assessment of current and future hazards. Also discussed are the benefits that the new capability will deliver. These include improved capabilities for early warning of impactful changes in the Earth system, more reliable assessments of meteorological hazard risks, and quantitative attribution statements to support the Global Annual to Decadal Climate Update and State of the Climate reports issued by the World Meteorological Organization.

Climate Change in a Semi-Arid Environment: Effects on Crop Rotation with Dairy Manure Applications

Highlights Spring wheat yields increase with high temperatures and the minimal impact of increased CO2. At high temperatures and increased CO2, spring barley yields increase; yields decrease at only high temperatures. Potato and sugarbeet yields decrease with climate change. Climate change had little impact in soil nitrogen processes. Abstract. Agricultural crops grown in the irrigated semi-arid region of southern Idaho account for almost two-thirds of the median household income in the region. The impacts of climate change on cropping systems and the availability of water for irrigation would be a serious challenge for the state's economic dependence on agriculture. The objective of the study was to simulate the future impact of climate change on a crop rotation of spring wheat-potato-spring barley-sugarbeet grown in the semi-arid region of southern Idaho using conventional management practices and a high dairy manure application. The Root Zone Water Quality Model (RZWQM2) simulations used bias-corrected and spatially disaggregated projections from the World Climate Research Program’s coupled model inter-comparison project phase 5 to generate 40 GCM projections for the time from 2071-2099. The 28-yr scenarios were designed to simulate the impact of temperature and CO2 regimes on crop production, soil nitrogen mineralization, nitrogen seepage, deep seepage of water, and nitrous oxide emissions. Data from a field experiment in southern Idaho with conventional fertilizer practices and annual applications of 52 Mg ha-1 dairy manure with a crop rotation of spring wheat-potato-spring barley-sugarbeet were used in the RZWQM2 simulations. Results were compared to a baseline scenario of conventional management practices, historical weather data, and ambient CO2. Spring wheat yield increased by 22% and 16% for manure and fertilizer treatments, respectively, compared to the baseline scenario. Using the same comparison, potato tuber yield decreased by 65% and 60% in the manure and fertilizer treatments, respectively, for the highest temperature and CO2 increase scenarios. Spring barley produced a 33% higher yield with increased temperature and CO2. However, yield decreased when temperature increased, but CO2 remained unchanged. Sugarbeet yields decreased by 16% and 18% for manure and fertilizer treatments, respectively, compared to the baseline scenario. Nitrogen mineralization, N seepage from the profile, and nitrous oxide emissions were strongly influenced by the manure applications, and there was little simulated impact of climate change on these processes. These simulation results indicate that genetic enhancements or alternative management will be needed to maintain potato and sugar beet production levels in semi-arid areas, while spring barley and wheat yields may increase, assuming adequate irrigation water supplies are available. Keywords: Nitrogen Mineralization, Potato, RZWQM2, Spring Barley, Spring Wheat, Sugarbeet.

The First 30 Years of GEWEX

Abstract The Global Energy and Water Cycle Exchanges (GEWEX) project was created more than 30 years ago within the framework of the World Climate Research Programme (WCRP). The aim of this initiative was to address major gaps in our understanding of Earth’s energy and water cycles given a lack of information about the basic fluxes and associated reservoirs of these cycles. GEWEX sought to acquire and set standards for climatological data on variables essential for quantifying water and energy fluxes and for closing budgets at the regional and global scales. In so doing, GEWEX activities led to a greatly improved understanding of processes and our ability to predict them. Such understanding was viewed then, as it remains today, essential for advancing weather and climate prediction from global to regional scales. GEWEX has also demonstrated over time the importance of a wider engagement of different communities and the necessity of international collaboration for making progress on understanding and on the monitoring of the changes in the energy and water cycles under ever increasing human pressures. This paper reflects on the first 30 years of evolution and progress that has occurred within GEWEX. This evolution is presented in terms of three main phases of activity. Progress toward the main goals of GEWEX is highlighted by calling out a few achievements from each phase. A vision of the path forward for the coming decade, including the goals of GEWEX for the future, are also described.

Symmetric and asymmetric response of Indian Summer Monsoon rainfall to different ENSO decay phases in observations and CMIP6 models

The present study assesses the ability of the World Climate Research Programme's Coupled Model Intercomparison Project-6 (CMIP6) models in simulating the Indian Summer Monsoon (ISM) rainfall variability and its symmetric or asymmetric relationship with the El Niño–Southern Oscillation (ENSO) decay phase. The El Niño decays are classified into three categories based on their transition characteristics (rapid/slow) with respect to the subsequent boreal summer season, namely El Niño early-decay, El Niño mid-decay, and El Niño no-decay. Similarly, La Niña decays are also classified into the La Niña early-decay, La Niña mid-decay, and La Niña no-decay. In the case of El Niño and La Niña early-decay events, the observed summer rainfall anomalies over India are similar in spatial distribution and amplitude but opposite in sign, suggesting the symmetric rainfall response. This symmetric response is mainly due to the influence of the westward extension of western north Pacific (WNP) anomalous anticyclone/cyclone accompanied by tropical Indian Ocean (TIO) warming/cooling. In contrast to this, summers of El Niño and La Niña mid-decay events exhibited asymmetric rainfall anomaly pattern over India. This asymmetry in rainfall pattern between these two summers is attributed to differences mainly in the moisture divergence and low-level circulation over the Indo-Western Pacific region. Similar to El Niño and La Niña mid-decay cases, the summers of observed El Niño and La Niña no-decay events displayed ISM rainfall asymmetry. Differences in spatial distribution and magnitude of Sea surface temperature anomalies and related atmospheric circulation between these events mainly responsible for ISM rainfall asymmetry. Analysis of CMIP6 revealed that only ∼ 14% of selected CMIP6 models have simulated the rainfall anomalies somewhat correctly compared to observations during summers of different decay phases of ENSO. Further, symmetric/asymmetric response of summer rainfall anomalies over India between decay phases of El Niño and La Niña are not well captured by CMIP6 models. It is also noted that the CMIP6 models have displayed a tendency to simulate positive (negative) Indian Ocean Dipole conditions during the summers of El Niño no-decay (La Niña no-decay) events unlike in the observations.

The DOE E3SM Model Version 2: Overview of the Physical Model and Initial Model Evaluation

AbstractThis work documents version two of the Department of Energy's Energy Exascale Earth System Model (E3SM). E3SMv2 is a significant evolution from its predecessor E3SMv1, resulting in a model that is nearly twice as fast and with a simulated climate that is improved in many metrics. We describe the physical climate model in its lower horizontal resolution configuration consisting of 110 km atmosphere, 165 km land, 0.5° river routing model, and an ocean and sea ice with mesh spacing varying between 60 km in the mid‐latitudes and 30 km at the equator and poles. The model performance is evaluated with Coupled Model Intercomparison Project Phase 6 Diagnosis, Evaluation, and Characterization of Klima simulations augmented with historical simulations as well as simulations to evaluate impacts of different forcing agents. The simulated climate has many realistic features of the climate system, with notable improvements in clouds and precipitation compared to E3SMv1. E3SMv1 suffered from an excessively high equilibrium climate sensitivity (ECS) of 5.3 K. In E3SMv2, ECS is reduced to 4.0 K which is now within the plausible range based on a recent World Climate Research Program assessment. However, a number of important biases remain including a weak Atlantic Meridional Overturning Circulation, deficiencies in the characteristics and spectral distribution of tropical atmospheric variability, and a significant underestimation of the observed warming in the second half of the historical period. An analysis of single‐forcing simulations indicates that correcting the historical temperature bias would require a substantial reduction in the magnitude of the aerosol‐related forcing.

Facilitating knowledge sharing and co-creation between communities of climate research and its users

Climate impacts and risks involve climatic impact drivers, the exposure and vulnerability of natural and socioeconomic systems, and a strong interdependency exists between climate, nature and human society. Effective climate change adaptation requires effective knowledge sharing and co-creation between communities of climate research and its users. This pilot project of the World Climate Research Programme, supported by the Asia-Pacific Network for Global Change Research and other funders, brought together experts and future leaders from the two communities to familiarise themselves with the aspects of the other discipline. The project demonstrated that knowledge-sharing and co-creation benefit everyone involved and good progress in bridging the gap between the two communities can be made. We also learned that going beyond one’s own training is challenging and fixing the gap requires both communities’ long-term and sustained commitment.

Supporting regional and international cooperation in research on extremes in climate prediction and projection ensembles: Workshop summary

Weather and climate extremes have enormous impacts on society, and are becoming more severe and frequent as the world warms. Most developing countries in the Asia-Pacific region are highly vulnerable to risks associated with heatwaves and cold spells, droughts and floods, tropical cyclones, wildfires, and other extremes. To support regional and international cooperation for research on weather and climate extremes in the Asia-Pacific region, the World Climate Research Programme (WCRP) hosted an online workshop on Extremes in Climate Prediction Ensembles (ExCPEns) from 25 to 28 October 2021 with the support of Asia-Pacific Network for Global Change Research (APN). The workshop aimed to advance the rapidly emerging science of exploiting subseasonal, seasonal, annual to decadal and long-term prediction ensembles to improve the prediction and understanding of weather and climate extreme events. An Early Career Scientist (ECS) event followed the ExCPEns workshop and consisted of a discussion and networking forum for ECS from APN member developing countries, along with a series of ECS training lectures and discussion sessions. Through the workshop and discussions among stakeholders, important scientific results on prediction and future changes in weather and climate extremes were communicated. Moreover, new research topics spanning these different time scales were identified and prioritized.

Cloud Screening Method in Complex Background Areas Containing Snow and Ice Based on Landsat 9 Images.

The first step in the application of Landsat 9 imagery is cloud screening, and the International Satellite Cloud Climatology Project (ISCCP) has made cloud screening an important part of the World Climate Research Program. The accurate identification of clouds in remote sensing satellite images containing snow and ice on the subsurface has been a challenging task in the cloud screening process. It is imperative to fully exploit the characteristic heterogeneous information of the cloud and snow subsurface, to solve the problem of cloud–snow confusion in the snow and ice environment, and to carry out research on cloud screening technology without interference from the snow and ice subsurface. In view of this, this paper will systematically carry out research on cloud screening methods in snow and ice environments. In this paper, we propose the building of a cloud screening algorithm that takes into account the difficulty of the fact that snow and ice on the subsurface can easily interfere with cloud recognition, and the influence of an empirical threshold or statistical threshold that makes its application less effective, and then establish a dynamic threshold cloud screening algorithm that is suitable for snow and ice environments. The research results will provide new ideas and perspectives to solve the problem of surface-type interference that most of the existing cloud screening algorithms contend with.

Attribution of multi-annual to decadal changes in the climate system: The Large Ensemble Single Forcing Model Intercomparison Project (LESFMIP)

Multi-annual to decadal changes in climate are accompanied by changes in extreme events that cause major impacts on society and severe challenges for adaptation. Early warnings of such changes are now potentially possible through operational decadal predictions. However, improved understanding of the causes of regional changes in climate on these timescales is needed both to attribute recent events and to gain further confidence in forecasts. Here we document the Large Ensemble Single Forcing Model Intercomparison Project that will address this need through coordinated model experiments enabling the impacts of different external drivers to be isolated. We highlight the need to account for model errors and propose an attribution approach that exploits differences between models to diagnose the real-world situation and overcomes potential errors in atmospheric circulation changes. The experiments and analysis proposed here will provide substantial improvements to our ability to understand near-term changes in climate and will support the World Climate Research Program Lighthouse Activity on Explaining and Predicting Earth System Change.

Ocean negative carbon emissions: A new UN Decade program

Ocean negative carbon emissions: A new UN Decade program

Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations

Abstract. The indirect effect of winter Arctic Oscillation (AO) events on the following summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer sea ice forecasts in a large number of coupled climate models show a considerable drop in predictive skill for forecasts initialised prior to the date of melt onset in spring, suggesting that some drivers of sea ice variability on longer timescales may not be well represented in these models. To this end, we introduce an unsupervised learning approach based on cluster analysis and complex networks to establish how well the latest generation of coupled climate models participating in phase 6 of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP6) are able to reflect the spatio-temporal patterns of variability in Northern Hemisphere winter sea-level pressure and Arctic summer sea ice concentration over the period 1979–2020, relative to ERA5 atmospheric reanalysis and satellite-derived sea ice observations, respectively. Two specific global metrics are introduced as ways to compare patterns of variability between models and observations/reanalysis: the adjusted Rand index – a method for comparing spatial patterns of variability – and a network distance metric – a method for comparing the degree of connectivity between two geographic regions. We find that CMIP6 models generally reflect the spatial pattern of variability in the AO relatively well, although they overestimate the magnitude of sea-level pressure variability over the north-western Pacific Ocean and underestimate the variability over northern Africa and southern Europe. They also underestimate the importance of regions such as the Beaufort, East Siberian, and Laptev seas in explaining pan-Arctic summer sea ice area variability, which we hypothesise is due to regional biases in sea ice thickness. Finally, observations show that historically, winter AO events (negatively) covary strongly with summer sea ice concentration in the eastern Pacific sector of the Arctic, although now under a thinning ice regime, both the eastern and western Pacific sectors exhibit similar behaviour. CMIP6 models however do not show this transition on average, which may hinder their ability to make skilful seasonal to inter-annual predictions of summer sea ice.

Assesment of the CMIP6 models to study interseasonal SSTvariabilities in the BoB

ABSTRACT The Coupled Model Intercomparison Project (CMIP) of the World Climate Research Program (WCRP) has provided a large number of AOGCMs, publicly available through CMIP6, for a better understanding of global and regional-scale ocean dynamics. This study analyzes the SST outputs of 22 models in CMIP6 with AVHRR database for the NorthWest Bay of Bengal and gives the individual model performances in terms of performance indices and skill scores. It is shown that the CAMS-CSM1, which was designed for the East Asian region, has the best performance at a skill score of 0.95, whereas NorCPM1 has the worst performance with a skill score of 0.55 for the interseasonal timescales in the Bay of Bengal. Correlations of the net heat flux (with a correlation of 0.5) and solar insolation (with a correlation of 0.9) with SST are carried out as they are the primary drivers of SST variability interseasonally. Finally, a case study is conducted to study the seasonal features of upwelling in NorthWest BoB, which shows that only a few models are capable of adequately reproducing the upwelling, and conclude that the CAMS-CSM1 is most suitable for the BoB.