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Efforts to manage living marine resources (LMRs) under climate change need projections of future ocean conditions, yet most global climate models (GCMs) poorly represent critical coastal habitats. GCM utility for LMR applications will increase with higher spatial resolution but obstacles including computational and data storage costs, obstinate regional biases, and formulations prioritizing global robustness over regional skill will persist. Downscaling can help address GCM limitations, but significant improvements are needed to robustly support LMR science and management. We synthesize past ocean downscaling efforts to suggest a protocol to achieve this goal. The protocol emphasizes LMR-driven design to ensure delivery of decision-relevant information. It prioritizes ensembles of downscaled projections spanning the range of ocean futures with durations long enough to capture climate change signals. This demands judicious resolution refinement, with pragmatic consideration for LMR-essential ocean features superseding theoretical investigation. Statistical downscaling can complement dynamical approaches in building these ensembles. Inconsistent use of bias correction indicates a need for objective best practices. Application of the suggested protocol should yield regional ocean projections that, with effective dissemination and translation to decision-relevant analytics, can robustly support LMR science and management under climate change.

Responses of tropical cyclones (TCs) to CO2 doubling are explored using coupled global climate models (GCMs) with increasingly refined atmospheric/land horizontal grids (~ 200 km, ~ 50 km and ~ 25 km). The three models exhibit similar changes in background climate fields thought to regulate TC activity, such as relative sea surface temperature (SST), potential intensity, and wind shear. However, global TC frequency decreases substantially in the 50 km model, while the 25 km model shows no significant change. The ~ 25 km model also has a substantial and spatially-ubiquitous increase of Category 3–4–5 hurricanes. Idealized perturbation experiments are performed to understand the TC response. Each model’s transient fully-coupled 2 × CO2 TC activity response is largely recovered by “time-slice” experiments using time-invariant SST perturbations added to each model’s own SST climatology. The TC response to SST forcing depends on each model’s background climatological SST biases: removing these biases leads to a global TC intensity increase in the ~ 50 km model, and a global TC frequency increase in the ~ 25 km model, in response to CO2-induced warming patterns and CO2 doubling. Isolated CO2 doubling leads to a significant TC frequency decrease, while isolated uniform SST warming leads to a significant global TC frequency increase; the ~ 25 km model has a greater tendency for frequency increase. Global TC frequency responds to both (1) changes in TC “seeds”, which increase due to warming (more so in the ~ 25 km model) and decrease due to higher CO2 concentrations, and (2) less efficient development of these“seeds” into TCs, largely due to the nonlinear relation between temperature and saturation specific humidity.

Most present forecast systems for estuaries predict conditions for only a few days into the future. However, there are many reasons to expect that skillful estuarine forecasts are possible for longer time periods, including increasingly skillful extended atmospheric forecasts, the potential for lasting impacts of atmospheric forcing on estuarine conditions, and the predictability of tidal cycles. In this study, we test whether skillful estuarine forecasts are possible for up to 35 days into the future by combining an estuarine model of Chesapeake Bay with 35‐day atmospheric forecasts from an operational weather model. When compared with both a hindcast simulation from the same estuarine model and with observations, the estuarine forecasts for surface water temperature are skillful up to about 2 weeks into the future, and the forecasts for bottom temperature, surface and bottom salinity, and density stratification are skillful for all or the majority of the forecast period. Bottom oxygen forecasts are skillful when compared to the model hindcast, but not when compared with observations. We also find that skill for all variables in the estuary can be improved by taking the mean of multiple estuarine forecasts driven by an ensemble of atmospheric forecasts. Finally, we examine the forecasts in detail using two case studies of extreme events, and we discuss opportunities for improving the forecast skill.

Tropical cyclones that rapidly intensify are typically associated with the highest forecast errors and cause a disproportionate amount of human and financial losses. Therefore, it is crucial to understand if, and why, there are observed upward trends in tropical cyclone intensification rates. Here, we utilize two observational datasets to calculate 24-hour wind speed changes over the period 1982–2009. We compare the observed trends to natural variability in bias-corrected, high-resolution, global coupled model experiments that accurately simulate the climatological distribution of tropical cyclone intensification. Both observed datasets show significant increases in tropical cyclone intensification rates in the Atlantic basin that are highly unusual compared to model-based estimates of internal climate variations. Our results suggest a detectable increase of Atlantic intensification rates with a positive contribution from anthropogenic forcing and reveal a need for more reliable data before detecting a robust trend at the global scale. Tropical cyclones that rapidly intensify are associated with the highest forecast errors and the strongest storms. This study shows that the proportion of tropical cyclones that rapidly intensify recently increased in the Atlantic basin, and that this trend is likely due to anthropogenic forcing.

We examine several springtime plant phenology indices calculated from a set of statistically downscaled daily minimum and maximum temperature projections. Multiple statistical downscaling methods are used to refine daily temperature projections from multiple global climate models (GCMs) run with multiple radiative forcing scenarios. Focusing on the northeastern United States, the statistically downscaled temperature projections are input to a commonly used Extended Spring Indices (SI-x) model, yielding yearly estimates of phenological indices such as First Leaf Date (an early spring indicator), First Bloom Date (a late spring indicator), and the occurrence of Late False Springs (a year in which a hard freeze occurs after first bloom, when plants are vulnerable to damage from freezing conditions). The matrix of results allows one to analyze how projected spring phenological index differences arising from the choice of statistical downscaling method (i.e., the statistical downscaling uncertainty) compare with the magnitudes of variations across the different GCMs (climate model uncertainty) and radiative forcing pathways (scenario uncertainty). As expected, the onset of spring in the late 21st century projections, as measured by First Leaf and First Bloom Dates, typically shifts multiple weeks earlier in the year compared with the historical period. Those two start-of-spring indices can be thought of as being largely, but not entirely, dependent on an accumulation of heat since 1 January. In contrast, a Late False Spring occurs in large part due to a short-term weather event - namely if any single day after the First Bloom Date has a minimum temperature below -2.2C. Accordingly, spring phenological indices calculated from statistically downscaled climate projections can be influenced by how well the GCM’s historical simulation represents temperature variations on different time scales (diurnal temperature range, synoptic time-scale temperature variability, inter-annual temperature variations) as well as how a particular statistical refinement method (e.g., a delta change factor method, a quantile-based bias correction method, or a constructed analog method) combines information gleaned from both the GCM time series and the observation-based training data to generate the statistically refined daily maximum and minimum temperature time series. Though this study is limited in scope (northeastern United States region, a finite set of statistical downscaling methods and GCMs), we believe the general findings likely are illustrative and applicable to a wider range of mid-latitude locations where plant responses in spring are mostly temperature and day length driven.

Statistical downscaling (SD) is commonly used to provide information for the assessment of climate change impacts. Using as input the output from large-scale dynamical climate models and observation-based data products, SD aims to provide a finer grain of detail and to mitigate systematic biases. It is generally recognized as providing added value. However, one of the key assumptions of SD is that the relationships used to train the method during a historical period are unchanged in the future, in the face of climate change. The validity of this assumption is typically quite difficult to assess in the normal course of analysis, as observations of future climate are lacking. We approach this problem using a “perfect model” experimental design in which high-resolution dynamical climate model output is used as a surrogate for both past and future observations.We find that while SD in general adds considerable value, in certain well-defined circumstances it can produce highly erroneous results. Furthermore, the breakdown of SD in these contexts could not be foreshadowed during the typical course of evaluation based on only available historical data. We diagnose and explain the reasons for these failures in terms of physical, statistical, and methodological causes. These findings highlight the need for caution in the use of statistically downscaled products and the need for further research to consider other hitherto unknown pitfalls, perhaps utilizing more advanced perfect model designs than the one we have employed.

Estuaries are productive and ecologically important ecosystems, incorporating environmental drivers from watersheds, rivers, and the coastal ocean. Climate change has potential to modify the physical properties of estuaries, with impacts on resident organisms. However, projections from general circulation models (GCMs) are generally too coarse to resolve important estuarine processes. Here, we statistically downscaled near-surface air temperature and precipitation projections to the scale of the Chesapeake Bay watershed and estuary. These variables were linked to Susquehanna River streamflow using a water balance model and finally to spatially resolved Chesapeake Bay surface temperature and salinity using statistical model trees. The low computational cost of this approach allowed rapid assessment of projected changes from four GCMs spanning a range of potential futures under a high CO2 emission scenario, for four different downscaling methods. Choice of GCM contributed strongly to the spread in projections, but choice of downscaling method was also influential in the warmest models. Models projected a ~2–5.5 °C increase in surface water temperatures in the Chesapeake Bay by the end of the century. Projections of salinity were more uncertain and spatially complex. Models showing increases in winter-spring streamflow generated freshening in the Upper Bay and tributaries, while models with decreased streamflow produced salinity increases. Changes to the Chesapeake Bay environment have implications for fish and invertebrate habitats, as well as migration, spawning phenology, recruitment, and occurrence of pathogens. Our results underline a potentially expanded role of statistical downscaling to complement dynamical approaches in assessing climate change impacts in dynamically challenging estuaries.

Empirical statistical downscaling (ESD) methods seek to refine global climate model (GCM) outputs via processes that glean information from a combination of observations and GCM simulations. They aim to create value-added climate projections by reducing biases and adding finer spatial detail. Analysis techniques, such as cross-validation, allow assessments of how well ESD methods meet these goals during observational periods. However, the extent to which an ESD method’s skill might differ when applied to future climate projections cannot be assessed readily in the same manner. Here we present a “perfect model” experimental design that quantifies aspects of ESD method performance for both historical and late 21st century time periods. The experimental design tests a key stationarity assumption inherent to ESD methods – namely, that ESD performance when applied to future projections is similar to that during the observational training period. Case study results employing a single ESD method (an Asynchronous Regional Regression Model variant) and climate variable (daily maximum temperature) demonstrate that violations of the stationarity assumption can vary geographically, seasonally, and with the amount of projected climate change. For the ESD method tested, the greatest challenges in downscaling daily maximum temperature projections are revealed to occur along coasts, in summer, and under conditions of greater projected warming. We conclude with a discussion of the potential use and expansion of the perfect model experimental design, both to inform the development of improved ESD methods and to provide guidance on the use of ESD products in climate impacts analyses and decision-support applications.

This study demonstrates skillful seasonal prediction of 2-m air temperature and precipitation over land in a new high-resolution climate model developed by the Geophysical Fluid Dynamics Laboratory and explores the possible sources of the skill. The authors employ a statistical optimization approach to identify the most predictable components of seasonal mean temperature and precipitation over land and demonstrate the predictive skill of these components. First, the improved skill of the high-resolution model over the previous lower-resolution model in seasonal prediction of the Niño-3.4 index and other aspects of interest is shown. Then, the skill of temperature and precipitation in the high-resolution model for boreal winter and summer is measured, and the sources of the skill are diagnosed. Last, predictions are reconstructed using a few of the most predictable components to yield more skillful predictions than the raw model predictions. Over three decades of hindcasts, the two most predictable components of temperature are characterized by a component that is likely due to changes in external radiative forcing in boreal winter and summer and an ENSO-related pattern in boreal winter. The most predictable components of precipitation in both seasons are very likely ENSO-related. These components of temperature and precipitation can be predicted with significant correlation skill at least 9 months in advance. The reconstructed predictions using only the first few predictable components from the model show considerably better skill relative to observations than raw model predictions. This study shows that the use of refined statistical analysis and a high-resolution dynamical model leads to significant skill in seasonal predictions of 2-m air temperature and precipitation over land.

Tropical cyclones (TCs) are a hazard to life and property and a prominent element of the global climate system; therefore, understanding and predicting TC location, intensity, and frequency is of both societal and scientific significance. Methodologies exist to predict basinwide, seasonally aggregated TC activity months, seasons, and even years in advance. It is shown that a newly developed high-resolution global climate model can produce skillful forecasts of seasonal TC activity on spatial scales finer than basinwide, from months and seasons in advance of the TC season. The climate model used here is targeted at predicting regional climate and the statistics of weather extremes on seasonal to decadal time scales, and comprises high-resolution (50 km × 50 km) atmosphere and land components as well as more moderate-resolution (~100 km) sea ice and ocean components. The simulation of TC climatology and interannual variations in this climate model is substantially improved by correcting systematic ocean biases through “flux adjustment.” A suite of 12-month duration retrospective forecasts is performed over the 1981–2012 period, after initializing the climate model to observationally constrained conditions at the start of each forecast period, using both the standard and flux-adjusted versions of the model. The standard and flux-adjusted forecasts exhibit equivalent skill at predicting Northern Hemisphere TC season sea surface temperature, but the flux-adjusted model exhibits substantially improved basinwide and regional TC activity forecasts, highlighting the role of systematic biases in limiting the quality of TC forecasts. These results suggest that dynamical forecasts of seasonally aggregated regional TC activity months in advance are feasible.

Decadal prediction experiments were conducted as part of phase 5 of the Coupled Model Intercomparison Project (CMIP5) using the GFDL Climate Model, version 2.1 (CM2.1) forecast system. The abrupt warming of the North Atlantic Subpolar Gyre (SPG) that was observed in the mid-1990s is considered as a case study to evaluate forecast capabilities and better understand the reasons for the observed changes. Initializing the CM2.1 coupled system produces high skill in retrospectively predicting the mid-1990s shift, which is not captured by the uninitialized forecasts. All the hindcasts initialized in the early 1990s show a warming of the SPG; however, only the ensemble-mean hindcasts initialized in 1995 and 1996 are able to reproduce the observed abrupt warming and the associated decrease and contraction of the SPG. Examination of the physical mechanisms responsible for the successful retrospective predictions indicates that initializing the ocean is key to predicting the mid-1990s warming. The successful initialized forecasts show an increased Atlantic meridional overturning circulation and North Atlantic Current transport, which drive an increased advection of warm saline subtropical waters northward, leading to a westward shift of the subpolar front and, subsequently, a warming and spindown of the SPG. Significant seasonal climate impacts are predicted as the SPG warms, including a reduced sea ice concentration over the Arctic, an enhanced warming over the central United States during summer and fall, and a northward shift of the mean ITCZ. These climate anomalies are similar to those observed during a warm phase of the Atlantic multidecadal oscillation, which is encouraging for future predictions of North Atlantic climate.

Using simulations performed with 18 coupled atmosphere–ocean global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5), projections of the Northern Hemisphere snowfall under the representative concentration pathway (RCP4.5) scenario are analyzed for the period 2006–2100. These models perform well in simulating twentieth-century snowfall, although there is a positive bias in many regions. Annual snowfall is projected to decrease across much of the Northern Hemisphere during the twenty-first century, with increases projected at higher latitudes. On a seasonal basis, the transition zone between negative and positive snowfall trends corresponds approximately to the −10°C isotherm of the late twentieth-century mean surface air temperature, such that positive trends prevail in winter over large regions of Eurasia and North America. Redistributions of snowfall throughout the entire snow season are projected to occur—even in locations where there is little change in annual snowfall. Changes in the fraction of precipitation falling as snow contribute to decreases in snowfall across most Northern Hemisphere regions, while changes in total precipitation typically contribute to increases in snowfall. A signal-to-noise analysis reveals that the projected changes in snowfall, based on the RCP4.5 scenario, are likely to become apparent during the twenty-first century for most locations in the Northern Hemisphere. The snowfall signal emerges more slowly than the temperature signal, suggesting that changes in snowfall are not likely to be early indicators of regional climate change.

Retrospective predictions of multiyear North Atlantic Ocean hurricane frequency are explored by applying a hybrid statistical–dynamical forecast system to initialized and noninitialized multiyear forecasts of tropical Atlantic and tropical-mean sea surface temperatures (SSTs) from two global climate model forecast systems. By accounting for impacts of initialization and radiative forcing, retrospective predictions of 5- and 9-yr mean tropical Atlantic hurricane frequency show significant correlations relative to a null hypothesis of zero correlation. The retrospective correlations are increased in a two-model average forecast and by using a lagged-ensemble approach, with the two-model ensemble decadal forecasts of hurricane frequency over 1961–2011 yielding correlation coefficients that approach 0.9. These encouraging retrospective multiyear hurricane predictions, however, should be interpreted with care: although initialized forecasts have higher nominal skill than uninitialized ones, the relatively short record and large autocorrelation of the time series limits confidence in distinguishing between the skill caused by external forcing and that added by initialization. The nominal increase in correlation in the initialized forecasts relative to the uninitialized experiments is caused by improved representation of the multiyear tropical Atlantic SST anomalies. The skill in the initialized forecasts comes in large part from the persistence of a mid-1990s shift by the initialized forecasts, rather than from predicting its evolution. Predicting shifts like that observed in 1994/95 remains a critical issue for the success of multiyear forecasts of Atlantic hurricane frequency. The retrospective forecasts highlight the possibility that changes in observing system impact forecast performance.

The decadal predictability of sea surface temperature (SST) and 2-m air temperature (T2m) in the Geophysical Fluid Dynamics Laboratory (GFDL) decadal hindcasts, which are part of the Fifth Coupled Model Intercomparison Project experiments, has been investigated using an average predictability time (APT) analysis. Comparison of retrospective forecasts initialized using the GFDL Ensemble Coupled Data Assimilation system with uninitialized historical forcing simulations using the same model allows identification of the internal multidecadal pattern (IMP) for SST and T2m. The IMP of SST is characterized by an interhemisphere dipole, with warm anomalies centered in the North Atlantic subpolar gyre region and North Pacific subpolar gyre region, and cold anomalies centered in the Antarctic Circumpolar Current region. The IMP of T2m is characterized by a general bipolar seesaw, with warm anomalies centered in Greenland and cold anomalies centered in Antarctica. The retrospective prediction skill of the initialized system, verified against independent observational datasets, indicates that the IMP of SST may be predictable up to 4 (10) yr lead time at 95% (90%) significance level, and the IMP of T2m may be predictable up to 2 (10) yr at the 95% (90%) significance level. The initialization of multidecadal variations of northward oceanic heat transport in the North Atlantic significantly improves the predictive skill of the IMP. The dominant roles of oceanic internal dynamics in decadal prediction are further elucidated by fixed-forcing experiments in which radiative forcing is returned abruptly to 1961 values. These results point toward the possibility of meaningful decadal climate outlooks using dynamical coupled models if they are appropriately initialized from a sustained climate observing system.

The authors present results for simulated climate and climate change from a newly developed high-resolution global climate model [Geophysical Fluid Dynamics Laboratory Climate Model version 2.5 (GFDL CM2.5)]. The GFDL CM2.5 has an atmospheric resolution of approximately 50 km in the horizontal, with 32 vertical levels. The horizontal resolution in the ocean ranges from 28 km in the tropics to 8 km at high latitudes, with 50 vertical levels. This resolution allows the explicit simulation of some mesoscale eddies in the ocean, particularly at lower latitudes. Analyses are presented based on the output of a 280-yr control simulation; also presented are results based on a 140-yr simulation in which atmospheric CO2 increases at 1% yr−1 until doubling after 70 yr. Results are compared to GFDL CM2.1, which has somewhat similar physics but a coarser resolution. The simulated climate in CM2.5 shows marked improvement over many regions, especially the tropics, including a reduction in the double ITCZ and an improved simulation of ENSO. Regional precipitation features are much improved. The Indian monsoon and Amazonian rainfall are also substantially more realistic in CM2.5. The response of CM2.5 to a doubling of atmospheric CO2 has many features in common with CM2.1, with some notable differences. For example, rainfall changes over the Mediterranean appear to be tightly linked to topography in CM2.5, in contrast to CM2.1 where the response is more spatially homogeneous. In addition, in CM2.5 the near-surface ocean warms substantially in the high latitudes of the Southern Ocean, in contrast to simulations using CM2.1.

The impact of climate warming on the upper layer of the Bering Sea is investigated by using a high-resolution coupled global climate model. The model is forced by increasing atmospheric CO2 at a rate of 1% per year until CO2 reaches double its initial value (after 70 years), after which it is held constant. In response to this forcing, the upper layer of the Bering Sea warms by about 2°C in the southeastern shelf and by a little more than 1°C in the western basin. The wintertime ventilation to the permanent thermocline weakens in the western Bering Sea. After CO2 doubling, the southeastern shelf of the Bering Sea becomes almost ice-free in March, and the stratification of the upper layer strengthens in May and June. Changes of physical condition due to the climate warming would impact the pre-condition of spring bio-productivity in the southeastern shelf.

A new field of study, "decadal prediction," is emerging in climate science. Decadal prediction lies between seasonal/interannual forecasting and longer-term climate change projections, and focuses on time-evolving regional climate conditions over the next 10-30 yr. Numerous assessments of climate information user needs have identified this time scale as being important to infrastructure planners, water resource managers, and many others. It is central to the information portfolio required to adapt effectively to and through climatic changes. At least three factors influence time-evolving regional climate at the decadal time scale: 1) climate change commitment (further warming as the coupled climate system comes into adjustment with increases of greenhouse gases that have already occurred), 2) external forcing, particularly from future increases of greenhouse gases and recovery of the ozone hole, and 3) internally generated variability. Some decadal prediction skill has been demonstrated to arise from the first two of these factors, and there is evidence that initialized coupled climate models can capture mechanisms of internally generated decadal climate variations, thus increasing predictive skill globally and particularly regionally. Several methods have been proposed for initializing global coupled climate models for decadal predictions, all of which involve global time-evolving threedimensional ocean data, including temperature and salinity. An experimental framework to address decadal predictability/prediction is described in this paper and has been incorporated into the coordinated Coupled Model Intercomparison Model, phase 5 (CMIP5) experiments, some of which will be assessed for the IPCC Fifth Assessment Report (AR5). These experiments will likely guide work in this emerging field over the next 5 yr.

A coupled climate model with poleward-intensified westerly winds simulates significantly higher storage of heat and anthropogenic carbon dioxide by the Southern Ocean in the future when compared with the storage in a model with initially weaker, equatorward-biased westerlies. This difference results from the larger outcrop area of the dense waters around Antarctica and more vigorous divergence, which remains robust even as rising atmospheric greenhouse gas levels induce warming that reduces the density of surface waters in the Southern Ocean. These results imply that the impact of warming on the stratification of the global ocean may be reduced by the poleward intensification of the westerlies, allowing the ocean to remove additional heat and anthropogenic carbon dioxide from the atmosphere.

The analyses presented here focus on the Southern Ocean as simulated in a set of global coupled climate model control experiments conducted by several international climate modeling groups. Dominated by the Antarctic Circumpolar Current (ACC), the vast Southern Ocean can influence large-scale surface climate features on various time scales. Its climatic relevance stems in part from it being the region where most of the transformation of the World Ocean’s water masses occurs. In climate change experiments that simulate greenhouse gas–induced warming, Southern Ocean air–sea heat fluxes and three-dimensional circulation patterns make it a region where much of the future oceanic heat storage takes place, though the magnitude of that heat storage is one of the larger sources of uncertainty associated with the transient climate response in such model projections. Factors such as the Southern Ocean’s wind forcing, heat, and salt budgets are linked to the structure and transport of the ACC in ways that have not been expressed clearly in the literature. These links are explored here in a coupled model context by analyzing a sizable suite of preindustrial control experiments associated with the forthcoming Intergovernmental Panel on Climate Change’s Fourth Assessment Report. A framework is developed that uses measures of coupled model simulation characteristics, primarily those related to the Southern Ocean wind forcing and water mass properties, to allow one to categorize, and to some extent predict, which models do better or worse at simulating the Southern Ocean and why. Hopefully, this framework will also lead to increased understanding of the ocean’s response to climate changes.

Historical climate simulations of the period 1861–2000 using two new Geophysical Fluid Dynamics Laboratory (GFDL) global climate models (CM2.0 and CM2.1) are compared with observed surface temperatures. All-forcing runs include the effects of changes in well-mixed greenhouse gases, ozone, sulfates, black and organic carbon, volcanic aerosols, solar flux, and land cover. Indirect effects of tropospheric aerosols on clouds and precipitation processes are not included. Ensembles of size 3 (CM2.0) and 5 (CM2.1) with all forcings are analyzed, along with smaller ensembles of natural-only and anthropogenic-only forcing, and multicentury control runs with no external forcing. Observed warming trends on the global scale and in many regions are simulated more realistically in the all-forcing and anthropogenic-only forcing runs than in experiments using natural-only forcing or no external forcing. In the all-forcing and anthropogenic-only forcing runs, the model shows some tendency for too much twentieth-century warming in lower latitudes and too little warming in higher latitudes. Differences in Arctic Oscillation behavior between models and observations contribute substantially to an underprediction of the observed warming over northern Asia. In the all-forcing and natural-only forcing runs, a temporary global cooling in the models during the 1880s not evident in the observed temperature records is volcanically forced. El Niño interactions complicate comparisons of observed and simulated temperature records for the El Chichón and Mt. Pinatubo eruptions during the early 1980s and early 1990s. The simulations support previous findings that twentieth-century global warming has resulted from a combination of natural and anthropogenic forcing, with anthropogenic forcing being the dominant cause of the pronounced late-twentieth-century warming. The regional results provide evidence for an emergent anthropogenic warming signal over many, if not most, regions of the globe. The warming signal has emerged rather monotonically in the Indian Ocean/western Pacific warm pool during the past half-century. The tropical and subtropical North Atlantic and the tropical eastern Pacific are examples of regions where the anthropogenic warming signal now appears to be emerging from a background of more substantial multidecadal variability.

The formulation and simulation characteristics of two new global coupled climate models developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) are described. The models were designed to simulate atmospheric and oceanic climate and variability from the diurnal time scale through multicentury climate change, given our computational constraints. In particular, an important goal was to use the same model for both experimental seasonal to interannual forecasting and the study of multicentury global climate change, and this goal has been achieved. Two versions of the coupled model are described, called CM2.0 and CM2.1. The versions differ primarily in the dynamical core used in the atmospheric component, along with the cloud tuning and some details of the land and ocean components. For both coupled models, the resolution of the land and atmospheric components is 2° latitude × 2.5° longitude; the atmospheric model has 24 vertical levels. The ocean resolution is 1° in latitude and longitude, with meridional resolution equatorward of 30° becoming progressively finer, such that the meridional resolution is 1/3° at the equator. There are 50 vertical levels in the ocean, with 22 evenly spaced levels within the top 220 m. The ocean component has poles over North America and Eurasia to avoid polar filtering. Neither coupled model employs flux adjustments. The control simulations have stable, realistic climates when integrated over multiple centuries. Both models have simulations of ENSO that are substantially improved relative to previous GFDL coupled models. The CM2.0 model has been further evaluated as an ENSO forecast model and has good skill (CM2.1 has not been evaluated as an ENSO forecast model). Generally reduced temperature and salinity biases exist in CM2.1 relative to CM2.0. These reductions are associated with 1) improved simulations of surface wind stress in CM2.1 and associated changes in oceanic gyre circulations; 2) changes in cloud tuning and the land model, both of which act to increase the net surface shortwave radiation in CM2.1, thereby reducing an overall cold bias present in CM2.0; and 3) a reduction of ocean lateral viscosity in the extratropics in CM2.1, which reduces sea ice biases in the North Atlantic. Both models have been used to conduct a suite of climate change simulations for the 2007 Intergovernmental Panel on Climate Change (IPCC) assessment report and are able to simulate the main features of the observed warming of the twentieth century. The climate sensitivities of the CM2.0 and CM2.1 models are 2.9 and 3.4 K, respectively. These sensitivities are defined by coupling the atmospheric components of CM2.0 and CM2.1 to a slab ocean model and allowing the model to come into equilibrium with a doubling of atmospheric CO2. The output from a suite of integrations conducted with these models is freely available online (see http://nomads.gfdl.noaa.gov/).

The climate response to idealized changes in the atmospheric CO2 concentration by the new GFDL climate model (CM2) is documented. This new model is very different from earlier GFDL models in its parameterizations of subgrid-scale physical processes, numerical algorithms, and resolution. The model was constructed to be useful for both seasonal-to-interannual predictions and climate change research. Unlike previous versions of the global coupled GFDL climate models, CM2 does not use flux adjustments to maintain a stable control climate. Results from two model versions, Climate Model versions 2.0 (CM2.0) and 2.1 (CM2.1), are presented. Two atmosphere–mixed layer ocean or slab models, Slab Model versions 2.0 (SM2.0) and 2.1 (SM2.1), are constructed corresponding to CM2.0 and CM2.1. Using the SM2 models to estimate the climate sensitivity, it is found that the equilibrium globally averaged surface air temperature increases 2.9 (SM2.0) and 3.4 K (SM2.1) for a doubling of the atmospheric CO2 concentration. When forced by a 1% per year CO2 increase, the surface air temperature difference around the time of CO2 doubling [transient climate response (TCR)] is about 1.6 K for both coupled model versions (CM2.0 and CM2.1). The simulated warming is near the median of the responses documented for the climate models used in the 2001 Intergovernmental Panel on Climate Change (IPCC) Working Group I Third Assessment Report (TAR). The thermohaline circulation (THC) weakened in response to increasing atmospheric CO2. By the time of CO2 doubling, the weakening in CM2.1 is larger than that found in CM2.0: 7 and 4 Sv (1 Sv ≡ 106 m3 s−1), respectively. However, the THC in the control integration of CM2.1 is stronger than in CM2.0, so that the percentage change in the THC between the two versions is more similar. The average THC change for the models presented in the TAR is about 3 or 4 Sv; however, the range across the model results is very large, varying from a slight increase (+2 Sv) to a large decrease (−10 Sv).

The current generation of coupled climate models run at the Geophysical Fluid Dynamics Laboratory (GFDL) as part of the Climate Change Science Program contains ocean components that differ in almost every respect from those contained in previous generations of GFDL climate models. This paper summarizes the new physical features of the models and examines the simulations that they produce. Of the two new coupled climate model versions 2.1 (CM2.1) and 2.0 (CM2.0), the CM2.1 model represents a major improvement over CM2.0 in most of the major oceanic features examined, with strikingly lower drifts in hydrographic fields such as temperature and salinity, more realistic ventilation of the deep ocean, and currents that are closer to their observed values. Regional analysis of the differences between the models highlights the importance of wind stress in determining the circulation, particularly in the Southern Ocean. At present, major errors in both models are associated with Northern Hemisphere Mode Waters and outflows from overflows, particularly the Mediterranean Sea and Red Sea.

In our original paper (Vecchi et al. 2013, hereafter V13),westated‘‘theskillintheinitializedforecastscomesinlargepart from the persistence of the mid-1990s shift by the ini-tializedforecasts,ratherthanfrompredictingitsevolution.’’Smith et al. (2013, hereafter S13) challenge that assertion,contending that the Met Office Decadal Prediction System(DePreSys) was able to make a successful retrospectiveforecastofthatshift.Westandbyouroriginalassertionandpresent additional analyses using output from DePreSysretrospective forecasts to support our assessment.S13 argue that the physical basis for multiyear hurri-cane prediction comes from (i) multiyear predictabilityofNorthAtlanticsubpolargyretemperature(SGT)and(ii) the impact of SGT changes on hurricane activity,thereby creating skillful multiyear predictions for hurri-canes. We agree that skillful multiyear predictions forNorthAtlanticSGTshavebeenmadewithDePreSysandother systems (e.g., Robson et al. 2012; Yeager et al.2012),includingtheGFDLpredictionsystem(Yangetal.2013; R. Msadek et al. 2013, unpublished manuscript).We also agree that there are physically plausible hypoth-eses by which SGT changes could influence hurricane ac-tivity (e.g., SuttonandHodson2005; Zhang andDelworth2006; Kang et al.2008; Dunstoneet al. 2011), although themagnitude of this influence is not clear.However, we disagree that this multiyear predictiveskill for SGTs has in fact produced skillful multiyearretrospective forecasts of hurricane activity, apart fromtheeffectofthepersistenceoftheshift.Hereweprovideadditional support for our assessment through the anal-ysis of retrospective forecasts of hurricane activity fromDePreSys (Smith et al. 2010), in addition to the workoriginally presented in V13. Output from the DePreSysretrospective forecasts was kindly provided by DougSmith.ThedatausedinthepresentreplyandinS13differfrom those in the original V13, with V13 using an SST-based index as a proxy for hurricane activity and S13 andthe present study counting low pressure systems in a dy-namical model. We have conducted analyses using theDePreSys hurricane retrospective forecasts for lead years1–5, 2–6, and 3–7; all leads yield similar results. In Fig. 1awe plot the number of forecast hurricanes for lead years1–5 (vertical axis) versus the number of observed hurri-canes (horizontal axis; 5-yr running mean centered onretrospective forecast year 3), with one symbol for eachinitializationyear.Asetofperfectretrospectiveforecastswould form a straight line with a 1:1 slope (i.e., for eachyear the observed and retrospective forecast hurricanecounts would be identical). Retrospective forecasts ini-tialized in the ‘‘preshift period’’ (blue circles) show anessentially random scatter, with no significant correlationbetween observed and forecast counts (the correlation is0.07). This indicates no skill.For the postshiftperiod (redcircles), the correlation is 20.47,againindicating noskill.Thus,fortheperiodsbeforeandaftertheshift,thereisnodemonstrated skill in multiyear retrospective forecasts ofhurricane counts, as was argued in V13 using a differenthurricane prediction methodology.

This paper summarizes the formulation of the ocean component to the Geophysical Fluid Dynamics Laboratory's (GFDL) climate model used for the 4th IPCC Assessment (AR4) of global climate change. In particular, it reviews the numerical schemes and physical parameterizations that make up an ocean climate model and how these schemes are pieced together for use in a state-of-the-art climate model. Features of the model described here include the following: (1) tripolar grid to resolve the Arctic Ocean without polar filtering, (2) partial bottom step representation of topography to better represent topographically influenced advective and wave processes, (3) more accurate equation of state, (4) three-dimensional flux limited tracer advection to reduce overshoots and undershoots, (5) incorporation of regional climatological variability in shortwave penetration, (6) neutral physics parameterization for representation of the pathways of tracer transport, (7) staggered time stepping for tracer conservation and numerical efficiency, (8) anisotropic horizontal viscosities for representation of equatorial currents, (9) parameterization of exchange with marginal seas, (10) incorporation of a free surface that accomodates a dynamic ice model and wave propagation, (11) transport of water across the ocean free surface to eliminate unphysical ``virtual tracer flux" methods, (12) parameterization of tidal mixing on continental shelves. We also present preliminary analyses of two particularly important sensitivities isolated during the development process, namely the details of how parameterized subgridscale eddies transport momentum and tracers.

The transient responses of two versions of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate model to a climate change forcing scenario are examined. The same computer codes were used to construct the atmosphere, ocean, sea ice and land surface components of the two models, and they employ the same types of sub-grid-scale parameterization schemes. The two model versions differ primarily, but not solely, in their spatial resolution. Comparisons are made of results from six coarse-resolution R15 climate change experiments and three medium-resolution R30 experiments in which levels of greenhouse gases (GHGs) and sulfate aerosols are specified to change over time. The two model versions yield similar global mean surface air temperature responses until the second half of the 21st century, after which the R15 model exhibits a somewhat larger response. Polar amplification of the Northern Hemisphere’s warming signal is more pronounced in the R15 model, in part due to the R15’s cooler control climate, which allows for larger snow and ice albedo positive feedbacks. Both models project a substantial weakening of the North Atlantic overturning circulation and a large reduction in the volume of Arctic sea ice to occur in the 21st century. Relative to their respective control integrations, there is a greater reduction of Arctic sea ice in the R15 experiments than in the R30 simulations as the climate system warms. The globally averaged annual mean precipitation rate is simulated to increase over time, with both model versions projecting an increase of about 8% to occur by the decade of the 2080s. While the global mean precipitation response is quite similar in the two models, regional differences exist, with the R30 model displaying larger increases in equatorial regions. D 2003 Elsevier Science B.V. All rights reserved.

A review is presented of the development and simulation characteristics of the most recent version of a global coupled model for climate variability and change studies at the Geophysical Fluid Dynamics Laboratory, as well as a review of the climate change experiments performed with the model. The atmospheric portion of the coupled model uses a spectral technique with rhomboidal 30 truncation, which corresponds to a transform grid with a resolution of approximately 3.75° longitude by 2.25° latitude. The ocean component has a resolution of approximately 1.875° longitude by 2.25° latitude. Relatively simple formulations of river routing, sea ice, and land surface processes are included. Two primary versions of the coupled model are described, differing in their initialization techniques and in the specification of sub-grid scale oceanic mixing of heat and salt. For each model a stable control integration of near millennial scale duration has been conducted, and the characteristics of both the time-mean and variability are described and compared to observations. A review is presented of a suite of climate change experiments conducted with these models using both idealized and realistic estimates of time-varying radiative forcing. Some experiments include estimates of forcing from past changes in volcanic aerosols and solar irradiance. The experiments performed are described, and some of the central findings are highlighted. In particular, the observed increase in global mean surface temperature is largely contained within the spread of simulated global mean temperatures from an ensemble of experiments using observationally-derived estimates of the changes in radiative forcing from increasing greenhouse gases and sulfate aerosols.

Sea-level rise is an important aspect of climate change because of its impact on society and ecosystems. Here we present an intercomparison of results from ten coupled atmosphere-ocean general circulation models (AOGCMs) for sea-level changes simulated for the twentieth century and projected to occur during the twenty first century in experiments following scenario IS92a for greenhouse gases and sulphate aerosols. The model results suggest that the rate of sea-level rise due to thermal expansion of sea water has increased during the twentieth century, but the small set of tide gauges with long records might not be adequate to detect this acceleration. The rate of sea-level rise due to thermal expansion continues to increase throughout the twenty first century, and the projected total is consequently larger than in the twentieth century; for 1990–2090 it amounts to 0.20–0.37 m. This wide range results from systematic uncertainty in modelling of climate change and of heat uptake by the ocean. The AOGCMs agree that sea-level rise is expected to be geographically non-uniform, with some regions experiencing as much as twice the global average, and others practically zero, but they do not agree about the geographical pattern. The lack of agreement indicates that we cannot currently have confidence in projections of local sea-level changes, and reveals a need for detailed analysis and intercomparison in order to understand and reduce the disagreements.

Time lags between changes in radiative forcing and the resulting simulated climate responses are investigated in a set of transient climate change experiments. Both surface air temperature (SAT) and soil moisture responses are examined. Results suggest that if the radiative forcing is held fixed at today's levels, the global mean SAT will rise an additional 1.0K before equilibrating. This unrealized warming commitment is larger than the 0.6K warming observed since 1900. The coupled atmosphere-ocean GCM's transient SAT response for the year 2000 is estimated to be similar to its equilibration response to 1980 radiative forcings—a lag of ∼20 years. Both the time lag and the warming commitment are projected to increase in the future, and depend on the model‧s climate sensitivity, oceanic heat uptake, and the forcing scenario. These results imply that much of the warming due to current greenhouse gas levels is yet to be realized.

We compared the temporal variability of the heat content of the world ocean, of the global atmosphere, and of components of Earth's cryosphere during the latter half of the 20th century. Each component has increased its heat content (the atmosphere and the ocean) or exhibited melting (the cryosphere). The estimated increase of observed global ocean heat content (over the depth range from 0 to 3000 meters) between the 1950s and 1990s is at least one order of magnitude larger than the increase in heat content of any other component. Simulation results using an atmosphere-ocean general circulation model that includes estimates of the radiative effects of observed temporal variations in greenhouse gases, sulfate aerosols, solar irradiance, and volcanic aerosols over the past century agree with our observation-based estimate of the increase in ocean heat content. The results we present suggest that the observed increase in ocean heat content may largely be due to the increase of anthropogenic gases in Earth's atmosphere.

Most projections of greenhouse gas‐induced climate change indicate a weakening of the thermohaline circulation (THC) in the North Atlantic in response to increased freshening and warming in the subpolar region. These changes reduce high-latitude upper-ocean density and therefore weaken the THC. Using ensembles of numerical experiments with a coupled ocean‐atmosphere model, it is found that this weakening could be delayed by several decades in response to a sustained upward trend in the Arctic/North Atlantic oscillation during winter, such as has been observed over the last 30 years. The stronger winds over the North Atlantic associated with this trend extract more heat from the ocean, thereby cooling and increasing the density of the upper ocean and thus opposing the previously described weakening of the THC. This result is of particular importance if the positive trend in the Arctic/North Atlantic oscillation is a response to increasing greenhouse gases, as has been recently suggested.

Analyses are conducted to assess whether simulated trends in SST and land surface air temperature from two versions of a coupled ocean-atmosphere model are consistent with the geographical distribution of observed trends over the period 1949–1997. The simulated trends are derived from model experiments with both constant and time-varying radiative forcing. The models analyzed are low-resolution (R 15, ∼4°) and medium-resolution (R30, ∼2°) versions of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate model. Internal climate variability is estimated from long control integrations of the models with no change of external forcing. The radiatively forced trends are based on ensembles of integrations using estimated past concentrations of greenhouse gases and direct effects of anthropogenic sulfate aerosols (G+S). For the regional assessment, the observed trends at each grid point with adequate temporal coverage during 1949–1997 are first compared with the R15 and R30 model unforced internal variability. Nearly 50% of the analyzed areas have observed warming trends exceeding the 95th percentile of trends from the control simulations. These results suggest that regional warming trends over much of the globe during 1949–1997 are very unlikely to have occurred due to internal climate variability alone and suggest a role for a sustained positive thermal forcing such as increasing greenhouse gases. The observed trends are then compared with the trend distributions obtained by combining the ensemble mean G+S forced trends with the internal variability “trend” distributions from the control runs. Better agreement is found between the ensemble mean G+S trends and the observed trends than between the model internal variability alone and the observed trends. However, the G+S trends are still significantly different from the observed trends over about 30% of the areas analyzed. Reasons for these regional inconsistencies between the simulated and the observed trends include possible deficiencies in (1) specified radiative forcings, (2) simulated responses to specified radiative forcings, (3) simulation of internal climate variability, or (4) observed temperature records.

The mechanism by which the model-simulated North Atlantic thermohaline circulation (THC) weakens in response to increasing greenhouse gas (GHG) forcing is investigated through the use of a set of five multi-century experiments. Using a coarse resolution version of the GFDL coupled climate model, the role of various surface fluxes in weakening the THC is assessed. Changes in net surface freshwater fluxes (precipitation, evaporation, and runoff from land) are found to be the dominant cause for the model's THC weakening. Surface heat flux changes brought about by rising GHG levels also contribute to THC weakening, but are of secondary importance. Wind stress variations have negligible impact on the THC's strength in the transient GHG experiment.

The effects of model initial conditions and the starting time of transient radiative forcings on global mean surface air temperature (SAT) and the North Atlantic thermohaline circulation (THC) are studied in a set of coupled climate GCM experiments. Nine climate change scenario experiments, in which the effective levels of greenhouse gases and tropospheric sulfate aerosols vary in time, are initialized from various points in a long control model run. The time at which the transition from constant to transient radiative forcing takes place is varied in the scenario runs, occurring at points representing either year 1766, 1866 or 1916. The sensitivity of projected 21st century global mean SATs and the THC to the choice of radiative forcing transition point is small, and is similar in magnitude to the variability arising from variations in the coupled GCM's initial three-dimensional state.

Anthropogenic CFC-11 dissolved in seawater is used to analyze ocean ventilation simulated in a global coupled air-sea model. Modeled CFC-11 distributions are compared to observations gathered on three Southern Hemisphere research cruises. The total amount of CFC-11 absorbed by the model's Southern Ocean is realistic, though some notable differences in the vertical structure exist. Observed and simulated CFC-11 distributions are qualitatively consistent with the coupled model's predictions that the ocean may delay greenhouse gas-induced warming of surface air temperatures at high southern latitudes. The sensitivity of model-predicted CFC-11 levels in the deep Southern Ocean to the choice of gas exchange parameterization suggests that quantitative assessments of model performance based upon simulated CFC-11 distributions can be limited by air-sea gas flux uncertainties in areas of rapid ocean ventilation. Such sensitivities can complicate the quantitative aspects of CFC-11 comparisons between models and observations, and between different models.

Identifying the prime drivers of the twentieth-century multidecadal variability in the Atlantic Ocean is crucial for predicting how the Atlantic will evolve in the coming decades and the resulting broad impacts on weather and precipitation patterns around the globe. Recently, Booth et al. showed that the Hadley Centre Global Environmental Model, version 2, Earth system configuration (HadGEM2-ES) closely reproduces the observed multidecadal variations of area-averaged North Atlantic sea surface temperature in the twentieth century. The multidecadal variations simulated in HadGEM2-ES are primarily driven by aerosol indirect effects that modify net surface shortwave radiation. On the basis of these results, Booth et al. concluded that aerosols are a prime driver of twentieth-century North Atlantic climate variability. However, here it is shown that there are major discrepancies between the HadGEM2-ES simulations and observations in the North Atlantic upper-ocean heat content, in the spatial pattern of multidecadal SST changes within and outside the North Atlantic, and in the subpolar North Atlantic sea surface salinity. These discrepancies may be strongly influenced by, and indeed in large part caused by, aerosol effects. It is also shown that the aerosol effects simulated in HadGEM2-ES cannot account for the observed anticorrelation between detrended multidecadal surface and subsurface temperature variations in the tropical North Atlantic. These discrepancies cast considerable doubt on the claim that aerosol forcing drives the bulk of this multidecadal variability.

Matei et al . (Reports, 6 January 2012, p. 76) claim to show skillful multiyear predictions of the Atlantic Meridional Overturning Circulation (AMOC). However, these claims are not justified, primarily because the predictions of AMOC transport do not outperform simple reference forecasts based on climatological annual cycles. Accordingly, there is no justification for the “confident” prediction of a stable AMOC through 2014.

[1] The sensitivity of the North Atlantic Ocean Circulation to an abrupt change in the Nordic Sea overflow is investigated for the first time using a high resolution eddy-permitting global coupled ocean-atmosphere model (GFDL CM2.5). The Nordic Sea overflow is perturbed through the change of the bathymetry in GFDL CM2.5. We analyze the Atlantic Meridional Overturning Circulation (AMOC) adjustment process and the downstream oceanic response to the perturbation. The results suggest that north of 34°N, AMOC changes induced by changes in the Nordic Sea overflow propagate on the slow tracer advection timescale, instead of the fast Kelvin wave timescale, resulting in a time lead of several years between subpolar and subtropical AMOC changes. The results also show that a stronger and deeper-penetrating Nordic Sea overflow leads to stronger and deeper AMOC, stronger northward ocean heat transport, reduced Labrador Sea deep convection, stronger cyclonic Northern Recirculation Gyre (NRG), westward shift of the North Atlantic Current (NAC) and southward shift of the Gulf Stream, warmer sea surface temperature (SST) east of Newfoundland and colder SST south of the Grand Banks, stronger and deeper NAC and Gulf Stream, and stronger oceanic eddy activities along the NAC and the Gulf Stream paths. A stronger/weaker Nordic Sea overflow also leads to a contracted/expanded subpolar gyre (SPG). This sensitivity study points to the important role of the Nordic Sea overflow in the large scale North Atlantic ocean circulation, and it is crucial for climate models to have a correct representation of the Nordic Sea overflow.

] In section 3, paragraph 9 of the paper “Assessing thepredictability of the Atlantic meridional overturning cir-culation and associated fingerprints” by R. Msadek et al.(Geophysical Research Letters, 37, L19608, doi:10.1029/2010GL044517, 2010), the sentence “M is the numberof members in each ensemble including the control run(M =11)” should be “M is the number of members in eachensemble (M =10).”

[1] The North Atlantic is among the few places where decadal climate variations are considered potentially predictable. The physical mechanisms of the decadal variability are hypothesized to be associated with fluctuations of the Atlantic meridional overturning circulation (AMOC). Perfect model predictability experiments using the GFDL CM2.1 climate model are analyzed to investigate the potential predictability of the AMOC. Results indicate that the AMOC is predictable up to 20 years. We further connect AMOC predictability to readily observable fields. We show that modeled surface and subsurface signatures of AMOC variations defined by characteristic patterns of sea surface height, subsurface temperature, and upper ocean heat content anomalies, have a potential predictability similar to the AMOC's. Since we have longer observational records for these quantities than for direct measurements of the AMOC, our study highlights a potentially new promising method for monitoring AMOC variations, and hence assessing the predictability of the real climate system.

[1] In many climate model simulations using realistic, time-varying climate change forcing agents for the 20th and 21st centuries, the North Atlantic thermohaline circulation (THC) weakens in the 21st century, with little change in the 20th century. Here we use a comprehensive climate model to explore the impact of various climate change forcing agents on the THC. We conduct ensembles of integrations with subsets of climate change forcing agents. Increasing greenhouse gases – in isolation – produce a significant THC weakening in the late 20th century, but this change is partially offset by increasing anthropogenic aerosols, which tend to strengthen the THC. The competition between increasing greenhouse gases and anthropogenic aerosols thus produces no significant THC change in our 20th century simulations when all climate forcings are included. The THC weakening becomes significant several decades into the 21st century, when the effects of increasing greenhouse gases overwhelm the aerosol effects.