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Climate observations, research, and models are used extensively to help understand key processes underlying changes to the climate on a range of time scales from months to decades, and to investigate and describe possible longer-t...
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Climate observations, research, and models are used extensively to help understand key processes underlying changes to the climate on a range of time scales from months to decades, and to investigate and describe possible longer-term future climates. The knowledge generated serves as a scientific basis for climate services that are provided with the aim of tailoring information for decision-makers and policy-makers. Climate models and climate services are crucial elements for supporting policy and other societal actions to mitigate and adapt to climate change, and for making society better prepared and more resilient to climate-related risks. We present recommendations for future research topics for climate modeling and for climate services. These recommendations were produced by a group of experts in climate modeling and climate services, selected based on their individual leadership roles or participation in international activities. The recommendations were reached through extensive analysis, consideration and discussion of current and desired research capabilities, and wider engagement and refinement of the recommendations was achieved through a targeted workshop of initial recommendations and an open meeting at the European Geosciences Union General Assembly. The findings emphasize how research and innovation activities in the fields of climate modeling and climate services can contribute to improving climate knowledge and information with saliency for users in order to enhance capacity to transition to a sustainable and resilient society. The findings are relevant worldwide but are deliberately intended to influence the European Commission's next major multi-annual framework program of research and innovation over the period 2021-27.
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Skill for initialized decadal predictions for atmospheric and terrestrial variability is posited to reside in successful prediction of sea surface temperatures (SSTs) associated with the low-frequency modes of coupled ocean-atmosp...
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Skill for initialized decadal predictions for atmospheric and terrestrial variability is posited to reside in successful prediction of sea surface temperatures (SSTs) associated with the low-frequency modes of coupled ocean-atmosphere variability, for example, Pacific Decadal Oscillation (PDO) or Atlantic Multi-decadal Oscillation (AMO). So far, assessments of the skill of atmospheric and terrestrial variability in decadal predictions, however, have not been encouraging. Similarly, in the context of seasonal climate variability, teleconnections between SSTs associated with PDO and AMO and terrestrial climate have also been noted, but the same SST information used in predictive mode has failed to demonstrate convincing gains in skill. Are these results an artifact of model biases, or more a consequence of some fundamental property of coupled evolution of ocean-atmosphere system in extratropical latitudes, and the manner in which extratropical SST anomalies modulate (or constrain) atmospheric variability? Based on revisiting an analysis of a simple model that replicates the essential characteristics of coupled ocean-atmosphere interaction in extratropical latitudes, it is demonstrated that lack of additional skill in predicting atmospheric and terrestrial variability is more a consequence of fundamental characteristics of coupled evolution of ocean-atmosphere system. The results based on simple models are also substantiated following an analysis of a set of seasonal hind-casts with a fully coupled model.
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Twentieth century climate exhibits a strong warming trend. There is a broad scientific consensus that the warming contains a significant contribution from enhanced atmospheric greenhouse gas (GHG) concentrations due to anthropogen...
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Twentieth century climate exhibits a strong warming trend. There is a broad scientific consensus that the warming contains a significant contribution from enhanced atmospheric greenhouse gas (GHG) concentrations due to anthropogenic emissions. The climate will continue to warm during the 21st century due to the large inertia of the Earth System and in response to additional GHG emissions, but by how much remains highly uncertain. This is mainly due to three factors: natural variability, model uncertainty, and GHG emission scenario uncertainty. Uncertainty due to natural variability dominates at short time scales of a few years up to a few decades, while at the longer centennial time scales scenario uncertainty provides the largest contribution to the total uncertainty. Model uncertainty is important at all lead times. Furthermore, our understanding of the Earth System dynamics is incomplete. Potentially important feedbacks such as the carbon cycle feedback are not well understood and not even taken into account in many model projections. Yet the scientific evidence is overwhelming that the global mean surface temperature will exceed a level toward the end of the 21st century that will be unprecedented during the history of mankind, even if strong measures are taken to reduce global GHG emissions. It is this long-term perspective that demands immediate political action.
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The change in predictability of monthly mean temperature in a future climate is quantified based on the Community Climate System Model, version 4. According to this model, the North Atlantic overtakes the El Nino-Southern Oscillat...
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The change in predictability of monthly mean temperature in a future climate is quantified based on the Community Climate System Model, version 4. According to this model, the North Atlantic overtakes the El Nino-Southern Oscillation (ENSO) as the dominant area of seasonal predictability by 2095. This change arises partly because ENSO becomes less variable and partly because the ENSO teleconnection pattern expands into the Atlantic. Over land, the largest change in temperature predictability occurs in the tropics and is predominantly due to a decrease in ENSO variability. The southern peninsula of Africa and northeast South America are predicted to experience significant drying in a future climate, which decreases the effective heat capacity and memory, and hence increases variance independently of ENSO changes. Extratropical land areas experience enhanced precipitation in a future climate, which decreases temperature variance by the same mechanism. Finally, the model predicts that surface temperatures near the poles will become more predictable and less variable in a future climate, primarily because melting sea ice exposes the underlying sea surface temperature, which is more predictable owing to its longer time scale. Some of these results, especially the change in ENSO variance, are known to be model dependent. This paper also advances the use of information theory to quantify predictability, including (1) deriving a quantitative relation between predictability of the first and second kinds; (2) showing how differences in predictability can be decomposed in two dramatically different ways, facilitating physical interpretation; and (3) proposing a sample estimate of mutual information whose significance can be tested using standard techniques.
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The change in predictability of monthly mean temperature in a future climate is quantified based on the Community Climate System Model, version 4. According to this model, the North Atlantic overtakes the El Nino-Southern Oscillat...
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The change in predictability of monthly mean temperature in a future climate is quantified based on the Community Climate System Model, version 4. According to this model, the North Atlantic overtakes the El Nino-Southern Oscillation (ENSO) as the dominant area of seasonal predictability by 2095. This change arises partly because ENSO becomes less variable and partly because the ENSO teleconnection pattern expands into the Atlantic. Over land, the largest change in temperature predictability occurs in the tropics and is predominantly due to a decrease in ENSO variability. The southern peninsula of Africa and northeast South America are predicted to experience significant drying in a future climate, which decreases the effective heat capacity and memory, and hence increases variance independently of ENSO changes. Extratropical land areas experience enhanced precipitation in a future climate, which decreases temperature variance by the same mechanism. Finally, the model predicts that surface temperatures near the poles will become more predictable and less variable in a future climate, primarily because melting sea ice exposes the underlying sea surface temperature, which is more predictable owing to its longer time scale. Some of these results, especially the change in ENSO variance, are known to be model dependent. This paper also advances the use of information theory to quantify predictability, including (1) deriving a quantitative relation between predictability of the first and second kinds; (2) showing how differences in predictability can be decomposed in two dramatically different ways, facilitating physical interpretation; and (3) proposing a sample estimate of mutual information whose significance can be tested using standard techniques.
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Forecasts for extremes in short-term climate (monthly means) are examined to understand the current prediction capability and potential predictability. This study focuses on 2-m surface temperature and precipitation extremes over ...
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Forecasts for extremes in short-term climate (monthly means) are examined to understand the current prediction capability and potential predictability. This study focuses on 2-m surface temperature and precipitation extremes over North and South America, and sea surface temperature extremes in the Nino-3.4 and Atlantic hurricane main development regions, using the Climate Forecast System (CFS) global climate model, for the period of 1982-2010. The primary skill measures employed are the anomaly correlation (AC) and root-mean-square error (RMSE). The success rate of forecasts is also assessed using contingency tables. The AC, a signal-to-noise skill measure, is routinely higher for extremes in short-term climate than those when all forecasts are considered. While the RMSE for extremes also rises, especially when skill is inherently low, it is found that the signal rises faster than the noise. Permutation tests confirm that this is not simply an effect of reduced sample size. Both 2-m temperature and precipitation forecasts have higher anomaly correlations in the area of South America than North America; credible skill in precipitation is very low over South America and absent over North America, even for extremes. Anomaly correlations for SST are very high in the Nino-3.4 region, especially for extremes, and moderate to high in the Atlantic hurricane main development region. Prediction skill for forecast extremes is similar to skill for observed extremes. Assessment of the potential predictability under perfect-model assumptions shows that predictability and prediction skill have very similar space-time dependence. While prediction skill is higher in CFS version 2 than in CFS version 1, the potential predictability is not.Digital Object Identifier http://dx.doi.org/10.1175/JCLI-D-12-00177.1
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Previous studies have outlined benefits of using multiple model platforms to make seasonal climate predictions. Here, reforecasts from five models included in the North American Multimodel Ensemble (NMME) project are utilized to d...
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Previous studies have outlined benefits of using multiple model platforms to make seasonal climate predictions. Here, reforecasts from five models included in the North American Multimodel Ensemble (NMME) project are utilized to determine skill in predicting Arctic sea ice extent (SIE) during 1982-2010. Overall, relative to the individual models, the multimodel average results in generally smaller biases and better correlations for predictions of total SIE and year-to-year (Y2Y), linearly, and quadratically detrended variability. Also notable is the increase in error for NMME predictions of total September SIE during the mid-1990s through 2000s. After 2000, observed September SIE is characterized by more significant negative trends and increased Y2Y variance, which suggests that recent sea ice loss is resulting in larger prediction errors. While this tendency is concerning, due to the possibility of models not accurately representing the changing trends in sea ice, the multimodel approach still shows promise in providing more skillful predictions of Arctic SIE over any individual model.
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The IAP numerical annual climate prediction system has been presented in this paper. In order to evaluate this annual prediction system, annual ensemble hindcast experiments over a 21-year period from 1980 to 2000 have been done. ...
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The IAP numerical annual climate prediction system has been presented in this paper. In order to evaluate this annual prediction system, annual ensemble hindcast experiments over a 21-year period from 1980 to 2000 have been done. Systematic assessment shows that this annual prediction system has higher predictability for summer climate in tropic than in extra-tropic area, and higher predictabilities over ocean than over land for the fields of precipitation, sea level pressure and surface air temperature; for 500 hPa geopotential height field, the predictability assuming a zonal distribution decreases from tropic to middle-high latitudes, and in China it is the highest among those of all fields. Correlation analysis shows that the prediction ability of IAP annual prediction system to summer temperature is higher than that to precipitation, and the prediction skill can be remarkably improved by the correction system. Furthermore, the comparison between annual and extraseasonal hindcasts indicates that precipitation hindcasted extraseasonally is better than that done annually, and the major discrepancy exists in middle-high latitudes.
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The IAP numerical annual climate prediction system has been presented in this paper. In order to evaluate this annual prediction system, annual ensemble hindcast experiments over a 21-year period from 1980 to 2000 have been done. ...
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The IAP numerical annual climate prediction system has been presented in this paper. In order to evaluate this annual prediction system, annual ensemble hindcast experiments over a 21-year period from 1980 to 2000 have been done. Systematic assessment shows that this annual prediction system has higher predictability for summer climate in tropic than in extra-tropic area, and higher predictabilities over ocean than over land for the fields of precipitation, sea level pressure and surface air temperature; for 500 hPa geopotential height field, the predictability assuming a zonal distribution decreases from tropic to middle-high latitudes, and in China it is the highest among those of all fields. Correlation analysis shows that the prediction ability of IAP annual prediction system to summer temperature is higher than that to precipitation, and the prediction skill can be remarkably improved by the correction system. Furthermore, the comparison between annual and extraseasonal hindcasts indicates that precipitation hindcasted extraseasonally is better than that done annually, and the major discrepancy exists in middle-high latitudes.
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Predictability properties of the Atlantic meridional overturning circulation (AMOC) are measured and compared to those of the upper-500-m heat content in the North Atlantic based on control simulations from nine comprehensive coup...
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Predictability properties of the Atlantic meridional overturning circulation (AMOC) are measured and compared to those of the upper-500-m heat content in the North Atlantic based on control simulations from nine comprehensive coupled climate models. By estimating the rate at which perfect predictions from initially similar states diverge, the authors find the prediction range at which initialization loses its potential to have a positive impact on predictions. For the annual-mean AMOC, this range varies substantially from one model to another, but on average, it is about a decade. For eight of the models, this range is less than the corresponding range for heat content. For 5- and 10-yr averages, predictability is substantially greater than for annual means for both fields, but the enhancement is more for AMOC; indeed, for the averaged fields, AMOC is more predictable than heat content. Also, there are spatial patterns of AMOC that have especially high predictability. For the most predictable of these patterns, AMOC retains predictability for more than two decades in a typical model. These patterns are associated with heat content fluctuations that also have above-average predictability, which suggests that AMOC may have a positive influence on the predictability of heat content for these special structures.
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