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L. Goddard A. Kumar A. Solomon D. Smith G. Boer P. Gonzalez V. Kharin W. Merryfield C. Deser S. J. Mason B. P. Kirtman R. Msadek R. Sutton E. Hawkins T. Fricker G. Hegerl C. A. T. Ferro D. B. Stephenson G. A. Meehl T. Stockdale R. Burgman A. M. Greene Y. Kushnir M. Newman J. Carton I. Fukumori T. Delworth 《Climate Dynamics》2013,40(1-2):245-272
Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model’s ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty. 相似文献
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Real-time multi-model decadal climate predictions 总被引：1，自引：1，他引：0
Doug M. Smith Adam A. Scaife George J. Boer Mihaela Caian Francisco J. Doblas-Reyes Virginie Guemas Ed Hawkins Wilco Hazeleger Leon Hermanson Chun Kit Ho Masayoshi Ishii Viatcheslav Kharin Masahide Kimoto Ben Kirtman Judith Lean Daniela Matei William J. Merryfield Wolfgang A. Müller Holger Pohlmann Anthony Rosati Bert Wouters Klaus Wyser 《Climate Dynamics》2013,41(11-12):2875-2888
We present the first climate prediction of the coming decade made with multiple models, initialized with prior observations. This prediction accrues from an international activity to exchange decadal predictions in near real-time, in order to assess differences and similarities, provide a consensus view to prevent over-confidence in forecasts from any single model, and establish current collective capability. We stress that the forecast is experimental, since the skill of the multi-model system is as yet unknown. Nevertheless, the forecast systems used here are based on models that have undergone rigorous evaluation and individually have been evaluated for forecast skill. Moreover, it is important to publish forecasts to enable open evaluation, and to provide a focus on climate change in the coming decade. Initialized forecasts of the year 2011 agree well with observations, with a pattern correlation of 0.62 compared to 0.31 for uninitialized projections. In particular, the forecast correctly predicted La Niña in the Pacific, and warm conditions in the north Atlantic and USA. A similar pattern is predicted for 2012 but with a weaker La Niña. Indices of Atlantic multi-decadal variability and Pacific decadal variability show no signal beyond climatology after 2015, while temperature in the Niño3 region is predicted to warm slightly by about 0.5 °C over the coming decade. However, uncertainties are large for individual years and initialization has little impact beyond the first 4 years in most regions. Relative to uninitialized forecasts, initialized forecasts are significantly warmer in the north Atlantic sub-polar gyre and cooler in the north Pacific throughout the decade. They are also significantly cooler in the global average and over most land and ocean regions out to several years ahead. However, in the absence of volcanic eruptions, global temperature is predicted to continue to rise, with each year from 2013 onwards having a 50 % chance of exceeding the current observed record. Verification of these forecasts will provide an important opportunity to test the performance of models and our understanding and knowledge of the drivers of climate change. 相似文献
4.
Bin Wang June-Yi Lee In-Sik Kang J. Shukla C.-K. Park A. Kumar J. Schemm S. Cocke J.-S. Kug J.-J. Luo T. Zhou B. Wang X. Fu W.-T. Yun O. Alves E. K. Jin J. Kinter B. Kirtman T. Krishnamurti N. C. Lau W. Lau P. Liu P. Pegion T. Rosati S. Schubert W. Stern M. Suarez T. Yamagata 《Climate Dynamics》2009,33(1):93-117
We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework. 相似文献
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Emilia K. Jin James L. Kinter III B. Wang C.-K. Park I.-S. Kang B. P. Kirtman J.-S. Kug A. Kumar J.-J. Luo J. Schemm J. Shukla T. Yamagata 《Climate Dynamics》2008,31(6):647-664
The overall skill of ENSO prediction in retrospective forecasts made with ten different coupled GCMs is investigated. The coupled GCM datasets of the APCC/CliPAS and DEMETER projects are used for four seasons in the common 22 years from 1980 to 2001. As a baseline, a dynamic-statistical SST forecast and persistence are compared. Our study focuses on the tropical Pacific SST, especially by analyzing the NINO34 index. In coupled models, the accuracy of the simulated variability is related to the accuracy of the simulated mean state. Almost all models have problems in simulating the mean and mean annual cycle of SST, in spite of the positive influence of realistic initial conditions. As a result, the simulation of the interannual SST variability is also far from perfect in most coupled models. With increasing lead time, this discrepancy gets worse. As one measure of forecast skill, the tier-1 multi-model ensemble (MME) forecasts of NINO3.4 SST have an anomaly correlation coefficient of 0.86 at the month 6. This is higher than that of any individual model as well as both forecasts based on persistence and those made with the dynamic-statistical model. The forecast skill of individual models and the MME depends strongly on season, ENSO phase, and ENSO intensity. A stronger El Niño is better predicted. The growth phases of both the warm and cold events are better predicted than the corresponding decaying phases. ENSO-neutral periods are far worse predicted than warm or cold events. The skill of forecasts that start in February or May drops faster than that of forecasts that start in August or November. This behavior, often termed the spring predictability barrier, is in part because predictions starting from February or May contain more events in the decaying phase of ENSO. 相似文献
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Ben P. Kirtman Edwin K. Schneider David M. Straus Dughong Min Robert Burgman 《Climate Dynamics》2011,37(11-12):2389-2416
The new interactive ensemble modeling strategy is used to diagnose how noise due to internal atmospheric dynamics impacts the forced climate response during the twentieth century (i.e., 1870?C1999). The interactive ensemble uses multiple realizations of the atmospheric component model coupled to a single realization of the land, ocean and ice component models in order to reduce the noise due to internal atmospheric dynamics in the flux exchange at the interface of the component models. A control ensemble of so-called climate of the twentieth century simulations of the Community Climate Simulation Model version 3 (CCSM3) are compared with a similar simulation with the interactive ensemble version of CCSM3. Despite substantial differences in the overall mean climate, the global mean trends in surface temperature, 500?mb geopotential and precipitation are largely indistinguishable between the control ensemble and the interactive ensemble. Large differences in the forced response; however, are detected particularly in the surface temperature of the North Atlantic. Associated with the forced North Atlantic surface temperature differences are local differences in the forced precipitation and a substantial remote rainfall response in the deep tropical Pacific. We also introduce a simple variance analysis to separately compare the variance due to noise and the forced response. We find that the noise variance is decreased when external forcing is included. In terms of the forced variance, we find that the interactive ensemble increases this variance relative to the control. 相似文献
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Total sea surface temperature (SST) in a coupled GCM is diagnosed by separating the variability into signal variance and noise
variance. The signal and the noise is calculated from multi-decadal simulations from the COLA anomaly coupled GCM and the
interactive ensemble model by assuming both simulations have a similar signal variance. The interactive ensemble model is
a new coupling strategy that is designed to increase signal to noise ratio by using an ensemble of atmospheric realizations
coupled to a single ocean model. The procedure for separating the signal and the noise variability presented here does not
rely on any ad hoc temporal or spatial filter. Based on these simulations, we find that the signal versus the noise of SST
variability in the North Pacific is significantly different from that in the equatorial Pacific. The noise SST variability
explains the majority of the total variability in the North Pacific, whereas the signal dominates in the deep tropics. It
is also found that the spatial characteristics of the signal and the noise are also distinct in the North Pacific and equatorial
Pacific. 相似文献
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Global structure of the Madden-Julian Oscillations during two recent contrasting summer monsoon seasons over India 总被引：1，自引：0，他引：1
A. D. Vernekar V. Thapliyal R. H. Kripalani S. V. Singh B. Kirtman 《Meteorology and Atmospheric Physics》1993,52(1-2):37-47
Summary The global nature of the Madden-Julian Oscillations (MJOs) have been investigated by applying a frequency filter to daily data for the summer monsoon months (June to September) during two contrasting years—1987, a deficient monsoon year and 1988, an excess monsoon year. Several meteorological parameters at five levels in the troposphere have been examined. Regions with large amplitude of these oscillations are isolated for each year. The results indicate that the global spatial distribution of these oscillations is more in a deficient year than in an excess year, in particular over the Indian subcontinent and the EI Niño Southern Oscillation (ENSO) regions. The principal modes of variability during these two years have been investigated through Empirical Orthogonal Functions (EOFs). The first two eigenmodes of 850 hPa zonal wind explain nearly 50% of the variance. The dipole type of structure between the Indian and the Pacific region is more apparent in 1987 than in 1988. Time-longitude cross sections of the filtered zonal wind over the equatorial regions clearly show that eastward propagation is detected in 1987, but is virtually absent in 1988. It is also seen that the 30–60 day filtered winds are stronger during the monsoon of 1987 than in 1988. 相似文献
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Sea surface temperature (SST) anomalies can induce anomalous convection through surface evaporation and low-level moisture
convergence. This SST forcing of the atmosphere is indicated in a positive local rainfall–SST correlation. Anomalous convection
can feedback on SST through cloud-radiation and wind-evaporation effects and wind-induced oceanic mixing and upwelling. These
atmospheric feedbacks are reflected in a negative local rainfall–SST tendency correlation. As such, the simultaneous rainfall–SST
and rainfall–SST tendency correlations can indicate the nature of local air–sea interactions. Based on the magnitude of simultaneous
rainfall–SST and rainfall–SST tendency correlations, the present study identifies three distinct regimes of local air–sea
interactions. The relative importance of SST forcing and atmospheric forcing differs in these regimes. In the equatorial central-eastern
Pacific and, to a smaller degree, in the western equatorial Indian Ocean, SST forcing dominates throughout the year and the
surface heat flux acts mainly as a damping term. In the tropical Indo-western Pacific Ocean regions, SST forcing and atmospheric
forcing dominate alternatively in different seasons. Atmospheric forcing dominates in the local warm/rainy season. SST forcing
dominates with a positive wind-evaporation feedback during the transition to the cold/dry season. SST forcing also dominates
during the transition to the warm/rainy season but with a negative cloud-radiation feedback. The performance of atmospheric
general circulation model simulations forced by observed SST is closely linked to the regime of air–sea interaction. The forced
simulations have good performance when SST forcing dominates. The performance is low or poor when atmospheric forcing dominates. 相似文献
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Caribbean Sea rainfall variability during the rainy season and relationship to the equatorial Pacific and tropical Atlantic SST 总被引：1，自引：1，他引：0
The present study investigates the Caribbean Sea rainfall variability during the early and late rainy seasons and its association with sea surface temperature (SST) and air?Csea interaction based on observational estimates, the NCEP Climate Forecast System (CFS) and Global Forecast System (GFS) simulations, and the CFS retrospective forecasts. Analysis of the observational estimates indicates that air?Csea interaction is important over the Caribbean Sea, whereas the atmospheric forcing of SST dominates over the Gulf of Mexico. The CFS simulation captures the basic elements of this observed air?Csea relationship. The GFS simulation produces spurious SST forcing of the atmosphere over the Gulf of Mexico largely due to prescribing SST. The CFS forecasts capture the air?Csea relationship in the late rainy season (August?COctober), but cannot reproduce the SST forcing of atmosphere over the Caribbean Sea in the early rainy season (May?CJuly). An empirical orthogonal function (EOF) analysis indicates that the leading modes of percent anomalies of the rainy season precipitation have the largest loading in the southern Caribbean Sea in observations. The model simulations and forecasts skillfully reproduce the spatial pattern, but not the temporal evolution. The Caribbean Sea rainfall variability in the early rainy season is mainly due to the tropical North Atlantic (TNA) SST anomalies in observations, is contributed by both the TNA and eastern equatorial Pacific (EEP) SST anomalies in the CFS simulation, and has an overly large impact from the EEP SST anomalies in the GFS simulation and the CFS forecasts. The observed Caribbean Sea rainfall variability in the late rainy season has a leading impact from the EEP SST anomalies, with a secondary contribution from the TNA SST anomalies. In comparison, the model simulations and forecasts overestimate the impacts of the EEP SST anomalies due to an earlier development and longer duration of the El Ni?o-Southern Oscillation in the CFS compared to observations. 相似文献