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The Climate Science for Service Partnership Brazil (CSSP-Brazil) project provides Brazil and UK partners the opportunity to address important challenges faced by the climate modeling community, including the need to develop subseasonal and seasonal prediction and climate projection services. This paper provides an overview of the climate modeling and prediction research conducted through CSSP-Brazil within the context of a framework to advance climate prediction services in Brazil that includes a research-to-services (R2S) and a services-to-research (S2R) feedback pathway. The paper also highlights plans to advance scientific understanding and capability to produce beneficial climate knowledge and new products to improve climate prediction services to support decisions in various industries in Brazil. Policy-relevant outcomes from climate modeling and prediction exercises illustrated in this paper include supporting stakeholders with climate information provided from weeks to months ahead for (a) improving water management strategies for human consumption, navigation, and agricultural and electricity production; (b) defining crop variety and calendars for food production; and (c) diversifying energy production with alternatives to hydropower.

Recent decades have shown significant changes to the hydrological cycle over East Asia (EA), and further changes are expected due to future global warming. This study evaluates projected seasonal changes in the EA hydrological cycle using simulations that are 1.5 °C, 2.0 °C and 3.0 ∘C warmer than pre-industrial, from the Met Office Unified Model (MetUM) Global Ocean Mixed Layer model version 2.0 (GOML2.0), compared against present-day conditions. The moisture sources of the warming-induced precipitation changes are identified over five hydrologically unique regions within EA. Precipitation over EA increases with warming (except over southeastern EA in the spring and autumn) due to the intensified hydrological cycle. The projected seasonal changes in the hydrological cycle are usually nonlinear, with the rate of change between 1.5 ∘C and 2.0 ∘C larger than the rate of change between 2.0 ∘C and 3.0 ∘C of warming. The warming-induced precipitation increases are mainly associated with an increase in remote moisture convergence rather than local moisture recycling, except over the Tibetan Plateau. Decomposition of the changes in moisture sources by direction and flux component indicate that changes from the west are dominated by changes to moisture and changes from the north are more circulation driven. The changes from the south are moisture driven over southern EA and driven by moisture and circulation change over northern EA. Our results highlight the regionally and seasonally diverse projected changes to the EA hydrological cycle due to global warming, which will be useful for region-specific climate mitigation policies and the implementation of seasonally varying adaptation methods.

North Atlantic sea surface temperatures (SSTs) underwent pronounced multidecadal variability during the twentieth and early twenty-first century. We examine the impacts of this Atlantic Multidecadal Variability (AMV), also referred to as the Atlantic Multidecadal Oscillation (AMO), on climate in an ensemble of five coupled climate models at both low and high spatial resolution. We use a SST nudging scheme specified by the Coupled Model Intercomparision Project’s Decadal Climate Prediction Project Component C (CMIP6 DCPP-C) to impose a persistent positive/negative phase of the AMV in the North Atlantic in coupled model simulations; SSTs are free to evolve outside this region. The large-scale seasonal mean response to the positive AMV involves widespread warming over Eurasia and the Americas, with a pattern of cooling over the Pacific Ocean similar to the Pacific Decadal Oscillation (PDO), together with a northward displacement of the inter-tropical convergence zone (ITCZ). The accompanying changes in global atmospheric circulation lead to widespread changes in precipitation. We use Analysis of Variance (ANOVA) to demonstrate that this large-scale climate response is accompanied by significant differences between models in how they respond to the common AMV forcing, particularly in the tropics. These differences may arise from variations in North Atlantic air-sea heat fluxes between models despite a common North Atlantic SST forcing pattern. We cannot detect a widespread effect of increased model horizontal resolution in this climate response, with the exception of the ITCZ, which shifts further northwards in the positive phase of the AMV in the higher resolution configurations., The Authors would like to acknowledge the use of the UKRI funded JASMIN data analysis facility which was essential to the analysis and storage of PRIMAVERA project data. Ongoing curation of project data has been supported by the IS-ENES3 project that has received funding from the European Union’ Horizon 2020 research and innovation programme under Grant Agreement No. 824084. Authors DH, PM, JS, PD, YRR, CDR acknowledge funding from the PRIMAVERA project (www.primavera-h2020.eu), funded by the European Union’s Horizon 2020 programme under Grant Agreement 641727. PM was supported by U.K.–China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. PD thanks ECMWF for providing computing time in the framework of the special projects SPITDAVI. YRR was funded by the European Union’s Horizon 2020 Research and Inovation Programme in the framework of the Marie Skłodowska-Curie grant INADEC (Grant Agreement 80015400). Author DH would like to thank Nick Klingaman and Linda Hirons for their extensive help with the MetUM-GOML model. This article was written with support (DH) from National Environmental Research Council (NERC) national capability grant for the North Atlantic Climate System: Integrated study (ACSIS) program (Grants NE/N018001/1, NE/N018044/1, NE/N018028/1, and NE/N018052/1). MMR is supported by a Juan de la Cierva Incorporacion research contract of MICINN (Spain). The authors wish to acknowledge use of the Ferret program for analysis and graphics in this paper. Ferret is a product of NOAA’s Pacific Marine Environmental Laboratory. (Information is available at http://ferret.pmel.noaa.gov/Ferret/) and also the CF-python analysis package http://ncas-cms.github.io/cf-python/. Assembly of MetUM-GOML and development of MC-KPP was supported by the National Centre for Atmospheric Science and led by Dr. Nicholas Klingaman. The authors would also like to thank the three anonymous revi, Peer Reviewed, "Article signat per 17 autors/es: Daniel L. R. Hodson, Pierre-Antoine Bretonnière, Christophe Cassou, Paolo Davini, Nicholas P. Klingaman, Katja Lohmann, Jorge Lopez-Parages, Marta Martín-Rey, Marie-Pierre Moine, Paul-Arthur Monerie, Dian A. Putrasahan, Christopher D. Roberts, Jon Robson, Yohan Ruprich-Robert, Emilia Sanchez-Gomez, Jon Seddon & Retish Senan", Postprint (published version)

A new regional coupled modelling framework is introduced – the Regional Coupled Suite (RCS). This provides a flexible research capability with which to study the interactions between atmosphere, land, ocean, and wave processes resolved at kilometre scale, and the effect of environmental feedbacks on the evolution and impacts of multi-hazard weather events. A configuration of the RCS focussed on the Indian region, termed RCS-IND1, is introduced. RCS-IND1 includes a regional configuration of the Unified Model (UM) atmosphere, directly coupled to the JULES land surface model, on a grid with horizontal spacing of 4.4 km, enabling convection to be explicitly simulated. These are coupled through OASIS3-MCT libraries to 2.2 km grid NEMO ocean and WAVEWATCH III wave model configurations. To examine a potential approach to reduce computation cost and simplify ocean initialization, the RCS includes an alternative approach to couple the atmosphere to a lower resolution Multi-Column K-Profile Parameterization (KPP) for the ocean. Through development of a flexible modelling framework, a variety of fully and partially coupled experiments can be defined, along with traceable uncoupled simulations and options to use external input forcing in place of missing coupled components. This offers a wide scope to researchers designing sensitivity and case study assessments. Case study results are presented and assessed to demonstrate the application of RCS-IND1 to simulate two tropical cyclone cases which developed in the Bay of Bengal, namely Titli in October 2018 and Fani in April 2019. Results show realistic cyclone simulations, and that coupling can improve the cyclone track and produces more realistic intensification than uncoupled simulations for Titli but prevents sufficient intensification for Fani. Atmosphere-only UM regional simulations omit the influence of frictional heating on the boundary layer to prevent cyclone over-intensification. However, it is shown that this term can i

This paper assesses how well the CPTEC/INPE Brazilian Global Atmospheric Model (BAM-1.2) and the atmospheric component of the UK Met Office Hadley Centre Global Environment Model (HadGEM3-GC3.1) represent the main South American monsoon features. Climatological (1981–2010) ensemble means of Atmospheric Model Intercomparison Project (AMIP)-type climate simulations are evaluated. The assessment evaluated the models’ ability to represent the South America austral summer and winter precipitation contrast and associated circulation, key South American monsoon system elements, the association between south-east Brazil and South America precipitation, and climatological (1997/1998 to 2013/2014) distributions of rainy season onset and demise dates over south-east Brazil (15°S–25°S, 40°W–50°W) and the core monsoon region (10°S–20°S, 45°W–55°W). Despite some identified deficiencies, both models depict the monsoon region and represent the main features, including (1) the north-west–south-east precipitation band and associated ascending motion over central South America; (2) the upper-level Bolivian High and the north-east South America trough during the summer; (3) the lower-level South Atlantic and Pacific subtropical anti-cyclones and (4) the low-level jet east of the Andes. Both models represent upper-level divergence and lower-level convergence over the core monsoon region, and upper-level convergence and lower-level divergence over the Pacific and Atlantic anti-cyclones associated with the regional Walker circulation during the pre-monsoon (spring) and peak monsoon (summer) seasons. Convection over South America is weaker in BAM-1.2 than observed, consistent with continental precipitation deficit. The models reproduce the dipole-like precipitation pattern between south-east Brazil and south-eastern South America during the austral summer but overestimate these patterns spatial extent over the South Atlantic. Both models simulate the main observed climatological features o

Land–atmosphere feedbacks, through water and energy exchanges, provide subseasonal-to-seasonal predictability of the hydrological cycle. We analyse subseasonal land–atmosphere coupling over South America (SA) during extended austral summer for the soil moisture-to-precipitation and soil moisture-to-air temperature feedback pathways. We evaluate subseasonal hindcasts from global forecasting systems from the UK Met Office, the National Centers for Environmental Prediction (NCEP), the European Centre for Medium Range Weather Forecasts and the Center for Weather Forecast and Climate Studies (CPTEC), for the common period of 1999–2010, against two reanalyses. Biases in land–atmosphere states are established in the first week of hindcasts and increase with lead time. By Week 5, all the models only demonstrate good performance over northern, northeastern and southeastern SA for soil moisture and evapotranspiration and over tropical and subtropical SA for temperature. The hindcasts show stronger coupling at longer lead–lag between variables than reanalyses. Our results highlight possible deficiencies in feedbacks between soil moisture and precipitation in CPTEC and NCEP forecasts over the Amazon due to initial dry soil moisture biases, and in feedbacks between soil moisture and temperature for all four investigated models over southeastern SA due to erroneous representations of evapotranspiration.