The Climate-system Historical Forecast Project: providing open access to seasonal forecast ensembles from centers around the globe

Fil: Tompkins, Adrian M.. The Abdus Salam; ItaliaFil: Ortiz de Zarate, Maria Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Centre National de la Recherche Scientifique; FranciaFil: Saurral, Ramiro Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Centre National de la Recherche Scientifique; FranciaFil: Vera, Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Centre National de la Recherche Scientifique; FranciaFil: Saulo, Andrea Celeste. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Ministerio de Defensa. Secretaria de Planeamiento. Servicio Meteorológico Nacional; ArgentinaFil: Merryfield, William J.. Canadian Centre for Climate Modelling and Analysis; CanadáFil: Sigmond, Michael. Canadian Centre for Climate Modelling and Analysis; CanadáFil: Lee, Woo Sung. Canadian Centre for Climate Modelling and Analysis; CanadáFil: Baehr, Johanna. Universitat Hamburg; AlemaniaFil: Braun, Alain. Météo-France; FranciaFil: Amy Butler. National Ocean And Atmospheric Administration; Estados UnidosFil: Déqué, Michel. Météo-France; FranciaFil: Doblas Reyes, Francisco J.. Institució Catalana de Recerca i Estudis Avancats; España. Barcelona Supercomputing Center - Centro Nacional de Supercomputacion; EspañaFil: Gordon, Margaret. Met Office; Reino UnidoFil: Scaife, Adam A.. University of Exeter; Reino UnidoFil: Yukiko Imada. Japan Meteorological Agency. Meteorological Research Institute. Climate Research Department; JapónFil: Masayoshi Ishii. Japan Meteorological Agency. Meteorological Research Institute. Climate Research Department; JapónFil: Tomoaki Ose. Japan Meteorological Agency. Meteorological Research Institute. Climate Research Department; JapónFil: Kirtman, Ben. University of Miami; Estados UnidosFil: Kumar, Arun. National Ocean And Atmospheric Administration; Estados UnidosFil: Müller, Wolfgang A.. Max-Planck-Institut für Meteorologie; AlemaniaFil: Pirani, Anna. Université Paris-Saclay; FranciaFil: Stockdale, Tim. European Centre for Medium-Range Weather; Reino UnidoFil: Rixen, Michel. World Meteorological Organization. World Climate Research Programme; SuizaFil: Yasuda, Tamaki. Japan Meteorological Agency. Climate Prediction Division; Japó

The Polar Amplification Model Intercomparison Project (PAMIP) contribution to CMIP6: Investigating the causes and consequences of polar amplification

This is the final version. Available on open access from the European Geosciences Union via the DOI in this recordPolar amplification-the phenomenon where external radiative forcing produces a larger change in surface temperature at high latitudes than the global average-is a key aspect of anthropogenic climate change, but its causes and consequences are not fully understood. The Polar Amplification Model Intercomparison Project (PAMIP) contribution to the sixth Coupled Model Intercomparison Project (CMIP6; Eyring et al., 2016) seeks to improve our understanding of this phenomenon through a coordinated set of numerical model experiments documented here. In particular, PAMIP will address the following primary questions: (1) what are the relative roles of local sea ice and remote sea surface temperature changes in driving polar amplification? (2) How does the global climate system respond to changes in Arctic and Antarctic sea ice? These issues will be addressed with multi-model simulations that are forced with different combinations of sea ice and/or sea surface temperatures representing present-day, pre-industrial and future conditions. The use of three time periods allows the signals of interest to be diagnosed in multiple ways. Lower-priority tier experiments are proposed to investigate additional aspects and provide further understanding of the physical processes. These experiments will address the following specific questions: what role does ocean-atmosphere coupling play in the response to sea ice? How and why does the atmospheric response to Arctic sea ice depend on the pattern of sea ice page1140 forcing? How and why does the atmospheric response to Arctic sea ice depend on the model background state? What have been the roles of local sea ice and remote sea surface temperature in polar amplification, and the response to sea ice, over the recent period since 1979? How does the response to sea ice evolve on decadal and longer timescales? A key goal of PAMIP is to determine the real-world situation using imperfect climate models. Although the experiments proposed here form a coordinated set, we anticipate a large spread across models. However, this spread will be exploited by seeking "emergent constraints" in which model uncertainty may be reduced by using an observable quantity that physically explains the intermodel spread. In summary, PAMIP will improve our understanding of the physical processes that drive polar amplification and its global climate impacts, thereby reducing the uncertainties in future projections and predictions of climate change and variability.DECC/Defra Met Office Hadley Centre Climate ProgrammeEuropean Union Horizon 2020Natural Environment Research Council (NERC)National Science Foundation (NSF)Korean Polar Research InstituteBMBFArCSInderDe

Fast and slow responses of Southern Ocean sea surface temperature to SAM in coupled climate models

We investigate how sea surface temperatures (SSTs) around Antarctica respond to the Southern An- nular Mode (SAM) on multiple timescales. To that end we examine the relationship between SAM and SST within unperturbed preindustrial control simulations of coupled general circulation models (GCMs) included in the Climate Modeling Intercomparison Project phase 5 (CMIP5). We develop a technique to extract the re- sponse of the Southern Ocean SST (55◦S−70◦S) to a hypothetical step increase in the SAM index. We demonstrate that in many GCMs, the expected SST step re- sponse function is nonmonotonic in time. Following a shift to a positive SAM anomaly, an initial cooling regime can transition into surface warming around Antarctica. However, there are large differences across the CMIP5 ensemble. In some models the step response function never changes sign and cooling persists, while in other GCMs the SST anomaly crosses over from negative to positive values only three years after a step increase in the SAM. This intermodel diversity can be related to differences in the models’ climatological thermal ocean stratification in the region of seasonal sea ice around Antarctica. Exploiting this relationship, we use obser- vational data for the time-mean meridional and vertical temperature gradients to constrain the real Southern Ocean response to SAM on fast and slow timescales

The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1

Abstract. Recent decades have seen significant developments in climate prediction capabilities at seasonal-to-interannual timescales. However, until recently the potential of such systems to predict Arctic climate had rarely been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Interannual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre), an assessment of Arctic sea ice extent and volume predictability estimates in these models, and an investigation into to what extent predictability is dependent on the initial state. The inclusion of additional models expands the range of sea ice volume and extent predictability estimates, demonstrating that there is model diversity in the potential to make seasonal-to-interannual timescale predictions. We also investigate whether sea ice forecasts started from extreme high and low sea ice initial states exhibit higher levels of potential predictability than forecasts started from close to the models' mean state, and find that the result depends on the metric. Although designed to address Arctic predictability, we describe the archived data here so that others can use this data set to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Niño–Southern Oscillation

A minimal model to diagnose the contribution of the stratosphere to tropospheric forecast skill

Many recent studies have confirmed that variability in the stratosphere is a significant source of surface sub-seasonal prediction skill during Northern Hemisphere winter. It may be beneficial, therefore, to think about times in which there might be windows-of-opportunity for skillful sub-seasonal predictions based on the initial or predicted state of the stratosphere. In this study, we propose a simple, minimal model that can be used to understand the impact of the stratosphere on tropospheric predictability. Our model purposefully excludes state dependent predictability in either the stratosphere or troposphere or in the coupling between the two. Model parameters are set up to broadly represent current sub-seasonal prediction systems by comparison with four dynamical models from the Sub-Seasonal to Seasonal Prediction Project database. The model can reproduce the increases in correlation skill in sub-sets of forecasts for weak and strong lower stratospheric polar vortex states over neutral states despite the lack of dependence of coupling or predictability on the stratospheric state. We demonstrate why different forecast skill diagnostics can give a very different impression of the relative skill in the three sub-sets. Forecasts with large stratospheric signals and low amounts of noise are demonstrated to also be windows-of-opportunity for skillful tropospheric forecasts, but we show that these windows can be obscured by the presence of unrelated tropospheric signals