Generation Of General Circulation Models Research Articles

Past and future of the Arctic sea ice in High-Resolution Model Intercomparison Project (HighResMIP) climate models

Abstract. We examine the past and projected changes in Arctic sea ice properties in six climate models participating in the High-Resolution Model Intercomparison Project (HighResMIP) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Within HighResMIP, each of the experiments is run using a reference resolution configuration (consistent with typical CMIP6 runs) and using higher-resolution configurations. The role of horizontal grid resolution in both the atmosphere model component and the ocean model component in reproducing past and future changes in the Arctic sea ice cover is analysed. Model outputs from the coupled historical (hist-1950) and future (highres-future) runs are used to describe the multi-model, multi-resolution representation of the Arctic sea ice and to evaluate the systematic differences (if any) that resolution enhancement causes. Our results indicate that there is not a strong relationship between the representation of sea ice cover and the ocean/atmosphere grids; the impact of horizontal resolution depends rather on the sea ice characteristic examined and the model used. However, the refinement of the ocean grid has a more prominent effect compared to the refinement of the atmospheric one, with eddy-permitting ocean configurations generally providing more realistic representations of sea ice area and sea ice edges. All models project substantial sea ice shrinking: the Arctic loses nearly 95 % of sea ice volume from 1950 to 2050. The model selection based on historical performance potentially improves the accuracy of the model projections and predicts that the Arctic will turn ice-free as early as 2047. Along with the overall sea ice loss, changes in the spatial structure of the total sea ice and its partition in ice classes are noticed: the marginal ice zone (MIZ) will dominate the ice cover by 2050, suggesting a shift to a new sea ice regime much closer to the current Antarctic sea ice conditions. The MIZ-dominated Arctic might drive development and modification of model physics and parameterizations in the new generation of general circulation models (GCMs).

Climate change in the High Mountain Asia in CMIP6

Abstract. Climate change over High Mountain Asia (HMA, including the Tibetan Plateau) is investigated over the period 1979–2014 and in future projections following the four Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. The skill of 26 Coupled Model Intercomparison Project phase 6 (CMIP6) models is estimated for near-surface air temperature, snow cover extent and total precipitation, and 10 of them are used to describe their projections until 2100. Similarly to previous CMIP models, this new generation of general circulation models (GCMs) shows a mean cold bias over this area reaching −1.9 [−8.2 to 2.9] ∘C (90 % confidence interval) in comparison with the Climate Research Unit (CRU) observational dataset, associated with a snow cover mean overestimation of 12 % [−13 % to 43 %], corresponding to a relative bias of 52 % [−53 % to 183 %] in comparison with the NOAA Climate Data Record (CDR) satellite dataset. The temperature and snow cover model biases are more pronounced in winter. Simulated precipitation rates are overestimated by 1.5 [0.3 to 2.9] mm d−1, corresponding to a relative bias of 143 % [31 % to 281 %], but this might be an apparent bias caused by the undercatch of solid precipitation in the APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources) observational reference. For most models, the cold surface bias is associated with an overestimation of snow cover extent, but this relationship does not hold for all models, suggesting that the processes of the origin of the biases can differ from one model to another. A significant correlation between snow cover bias and surface elevation is found, and to a lesser extent between temperature bias and surface elevation, highlighting the model weaknesses at high elevation. The models with the best performance for temperature are not necessarily the most skillful for the other variables, and there is no clear relationship between model resolution and model skill. This highlights the need for a better understanding of the physical processes driving the climate in this complex topographic area, as well as for further parameterization developments adapted to such areas. A dependency of the simulated past trends on the model biases is found for some variables and seasons; however, some highly biased models fall within the range of observed trends, suggesting that model bias is not a robust criterion to discard models in trend analysis. The HMA median warming simulated over 2081–2100 with respect to 1995–2014 ranges from 1.9 [1.2 to 2.7] ∘C for SSP1-2.6 to 6.5 [4.9 to 9.0] ∘C for SSP5-8.5. This general warming is associated with a relative median snow cover extent decrease from −9.4 % [−16.4 % to −5.0 %] to −32.2 % [−49.1 % to −25.0 %] and a relative median precipitation increase from 8.5 % [4.8 % to 18.2 %] to 24.9 % [14.4 % to 48.1 %] by the end of the century in these respective scenarios. The warming is 11 % higher over HMA than over the other Northern Hemisphere continental surfaces, excluding the Arctic area. Seasonal temperature, snow cover and precipitation changes over HMA show a linear relationship with the global surface air temperature (GSAT), except for summer snow cover which shows a slower decrease at strong levels of GSAT.

An Assessment of Representation of Oceanic Mesoscale Eddy‐Atmosphere Interaction in the Current Generation of General Circulation Models and Reanalyses

AbstractOceanic mesoscale eddies interact strongly with the atmosphere, inducing heat flux that acts to dissipate the eddy potential energy. So far it remains unknown how well this oceanic mesoscale eddy‐atmosphere (OMEA) interaction is represented in the current generation of general circulation models. Here we evaluate the intensity of OMEA interaction in numerical models widely used by the community. It is found that the intensity of OMEA interaction differs significantly among models in its overall magnitude and spatial distribution. In eddy‐rich regions such as Kuroshio Extension and Antarctic Circumpolar Current, the intermodel difference can reach 40%. Surface wind strength and marine atmospheric boundary layer adjustment to mesoscale heat flux anomaly are two important factors accounting for the intermodel difference. Models with stronger surface wind tend to have higher OMEA interaction. Moreover, neglecting the marine atmospheric boundary layer adjustment, ocean‐alone model simulations overestimate OMEA interaction especially at middle and high latitudes by 20%–50%.

Key processes for the eastward propagation of the Madden‐Julian Oscillation based on multimodel simulations

AbstractAs a prominent climate variability mode with widespread influences on global weather extremes, the Madden‐Julian Oscillation (MJO) remains poorly represented in the latest generation of general circulation models (GCMs), with a particular challenge in simulating its eastward propagating convective signals. In this study, by analyzing multimodel simulations from a recent global MJO model evaluation project, an effort is made to identify key processes for the eastward propagation of the MJO through analyses of moisture entropy (ME) processes under a “moisture mode” framework for the MJO. The column‐integrated horizontal ME advection is found to play a critical role for the eastward propagation of the MJO in both observations and good MJO models, with a primary contribution through advection of the lower tropospheric seasonal mean ME by the MJO anomalous circulations. By contrast, the horizontal ME advection effect for the eastward propagation is greatly underestimated in poor MJO GCMs, due to model deficiencies in simulating both the seasonal mean ME pattern and MJO circulations, leading to a largely stationary MJO mode in these GCMs. These results thus pinpoint an important guidance toward improved representation of the MJO in climate and weather forecast models. While this study mainly focuses on fundamental physics for the MJO propagation over the Indian Ocean, complex influences by the Maritime Continent on the MJO and also ME processes associated with the MJO over the western Pacific warrant further investigations.

Reliability of regional and global climate models to simulate precipitation extremes over India

AbstractExtreme precipitation events over India have resulted in loss of human lives and damaged infrastructures, food crops, and lifelines. The inability of climate models to credibly project precipitation extremes in India has not been helpful to longer‐term hazards resilience policy. However, there have been claims that finer‐resolution and regional climate models may improve projections. The claims are examined as hypotheses by comparing models with observations from 1951–2005. This paper evaluates the reliability of the latest generation of general circulation models (GCMs), Coupled Model Intercomparison Project Phase 5 (CMIP5), specifically a subset of the better performing CMIP5 models (called “BEST‐GCM”). The relative value of finer‐resolution regional climate models (RCMs) is examined by comparing Coordinated Regional Climate Downscaling Experiment (CORDEX) South Asia RCMs (“CORDEX‐RCMs”) versus the GCMs used by those RCMs to provide boundary conditions, or the host GCMs (“HOST‐GCMs”). Ensemble mean of BEST‐GCMs performed better for most of the extreme precipitation indices than the CORDEX‐RCMs or their HOST‐GCMs. Weaker performance shown by ensemble mean of CORDEX‐RCMs is largely associated with their high intermodel variation. The CORDEX‐RCMs occasionally exhibited slightly superior skills compared to BEST‐GCMs; on the whole RCMs failed to significantly outperform GCMs. Observed trends in the extremes were not adequately captured by any of the model ensembles, while neither the GCMs nor the RCMs were determined to be adequate to inform hydrologic design.

Assessment of CMIP5 Model Simulations of the North American Monsoon System

AbstractPrecipitation, geopotential height, and wind fields from 21 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are examined to determine how well this generation of general circulation models represents the North American monsoon system (NAMS). Results show no improvement since CMIP3 in the magnitude (root-mean-square error and bias) of the mean annual cycle of monthly precipitation over a core monsoon domain, but improvement in the phasing of the seasonal cycle in precipitation is notable. Monsoon onset is early for most models but is clearly visible in daily climatological precipitation, whereas monsoon retreat is highly variable and unclear in daily climatological precipitation. Models that best capture large-scale circulation patterns at a low level usually have realistic representations of the NAMS, but even the best models poorly represent monsoon retreat. Difficulty in reproducing monsoon retreat results from an inaccurate representation of gradients in low-level geopotential height across the larger region, which causes an unrealistic flux of low-level moisture from the tropics into the NAMS region that extends well into the postmonsoon season. Composites of the models with the best and worst representations of the NAMS indicate that adequate representation of the monsoon during the early to midseason can be achieved even with a large-scale circulation pattern bias, as long as the bias is spatially consistent over the larger region influencing monsoon development; in other words, as with monsoon retreat, it is the inaccuracy of the spatial gradients in geopotential height across the larger region that prevents some models from realistic representation of the early and midseason monsoon system.

Investigation of impact of environmental changes on precipitation pattern of Pakistan

In this paper, variability in precipitation pattern of Pakistan due to environmental and climatic changes has been studied. Maps have been generated to depict global precipitation variation. Precipitation data of 25 stations of Pakistan have been used. These data were taken from Meteorological Department, Islamabad, Pakistan. The results of two global climate models, namely Australia's Commonwealth Scientific and Industrial Research's third generation general circulation model and National Center for Atmospheric Research's first generation precipitation circulation model for A2 scenario have been applied to investigate the changes. It is observed that precipitation pattern will change significantly in the future. The occurrence of precipitation in all seasons for Pakistan is expected to increase with almost uniform distribution across a season. Average annual precipitation of the country will undergo an increase in the range of +57 to +71 % as compared to average of the base period.

The NASA-Goddard Multi-scale Modeling Framework–Land Information System: Global land/atmosphere interaction with resolved convection

The present generation of general circulation models (GCM) use parameterized cumulus schemes and run at hydrostatic grid resolutions. To improve the representation of cloud-scale moist processes and land–atmosphere interactions, a global, Multi-scale Modeling Framework (MMF) coupled to the Land Information System (LIS) has been developed at NASA-Goddard Space Flight Center. The MMF–LIS has three components, a finite-volume (fv) GCM (Goddard Earth Observing System Ver. 4, GEOS-4), a 2D cloud-resolving model (Goddard Cumulus Ensemble, GCE), and the LIS, representing the large-scale atmospheric circulation, cloud processes, and land surface processes, respectively. The non-hydrostatic GCE model replaces the single-column cumulus parameterization of fvGCM. The model grid is composed of an array of fvGCM gridcells each with a series of embedded GCE models. A horizontal coupling strategy, GCE ↔ fvGCM ↔ Coupler ↔ LIS, offered significant computational efficiency, with the scalability and I/O capabilities of LIS permitting land–atmosphere interactions at cloud-scale. Global simulations of 2007–2008 and comparisons to observations and reanalysis products were conducted. Using two different versions of the same land surface model but the same initial conditions, divergence in regional, synoptic-scale surface pressure patterns emerged within two weeks. The sensitivity of large-scale circulations to land surface model physics revealed significant functional value to using a scalable, multi-model land surface modeling system in global weather and climate prediction.

SDSM ability in simulate predictors for climate detecting over Khorasan province

We know, the global circulation models (GCMs) enable to simulate the global climate, with the field variables being represented on a grid points 300km apart. But, the most recent generation of general circulation models (GCMs) still has serious problems. GCMs Models are benefit for detecting Climate change and zone on a special parameter. Even if global climate models in the future are run at high resolution there will remain the need to ‘downscale’ the results from such models to individual sites or localities for impact studies. But it is necessary to know if this category of Data has enough ability to simulate predictor Variables in the selected Region. In this study we selected reliable synoptic stations throughout the Iran that had less 41 year Data for case study and Using HadCM3 for Global Circulation Model Data, NCEP/NCAR for Reanalysis and SDSM Model for downscaling GCMs. Period of 1961-2001 was selected for Evaluated periods. The Result was shown that there was good ability to simulate predictant such as minimum and maximum temperature and precipitation and there is no significant deference with 0.5 critical errors. With using Data constructed for the future in SPI (Standard Precipitation Index) we could detect climate change in this region for future and it will improve climate risk management.

A New Methodology for Estimating the Unpredictable Component of Seasonal Atmospheric Variability

Abstract Predictability limits for seasonal atmospheric climate variability depend on the fraction of variability that is due to factors external to the atmosphere (e.g., boundary conditions) and the fraction that is internal. From the analysis of observed data alone, however, separation of the total seasonal atmospheric variance into its external and internal components remains a difficult and controversial issue. In this paper a simple procedure for estimating atmospheric internal variability is outlined. This procedure is based on the expected value of the mean square error between the observed and the general circulation model simulated (or predicted) seasonal mean anomaly. The end result is a spatial map for the estimate of the observed seasonal atmospheric internal (or unpredictable) variability. As improved general circulation models become available, mean square error estimated from the new generation of general circulation models can be easily included in the procedure proposed herein, bringing the estimate for the internal variability closer to its true estimate.

Local Regimes of Atmospheric Variability: A Case Study of Southern California

Abstract The primary regimes of local atmospheric variability are examined in a 6-km regional atmospheric model of the southern third of California, an area of significant land surface heterogeneity, intense topography, and climate diversity. The model was forced by reanalysis boundary conditions over the period 1995–2003. The region is approximately the same size as a typical grid box of the current generation of general circulation models used for global climate prediction and reanalysis product generation, and so can be thought of as a laboratory for the study of climate at spatial scales smaller than those resolved by global simulations and reanalysis products. It is found that the simulated circulation during the October–March wet season, when variability is most significant, can be understood through an objective classification technique in terms of three wind regimes. The composite surface wind patterns associated with these regimes exhibit significant spatial structure within the model domain, consistent with the complex topography of the region. These regimes also correspond nearly perfectly with the simulation’s highly structured patterns of variability in hydrology and temperature, and therefore are the main contributors to the local climate variability. The regimes are approximately equally likely to occur regardless of the phase of the classical large-scale modes of atmospheric variability prevailing in the Pacific–North American sector. The high degree of spatial structure of the local regimes and their tightly associated climate impacts, as well as their ambiguous relationship with the primary modes of large-scale variability, demonstrate that the local perspective offered by the high-resolution model is necessary to understand and predict the climate variations of the region.

Constraining Climate Sensitivity from the Seasonal Cycle in Surface Temperature

Abstract The estimated range of climate sensitivity has remained unchanged for decades, resulting in large uncertainties in long-term projections of future climate under increased greenhouse gas concentrations. Here the multi-thousand-member ensemble of climate model simulations from the climateprediction.net project and a neural network are used to establish a relation between climate sensitivity and the amplitude of the seasonal cycle in regional temperature. Most models with high sensitivities are found to overestimate the seasonal cycle compared to observations. A probability density function for climate sensitivity is then calculated from the present-day seasonal cycle in reanalysis and instrumental datasets. Subject to a number of assumptions on the models and datasets used, it is found that climate sensitivity is very unlikely (5% probability) to be either below 1.5–2 K or above about 5–6.5 K, with the best agreement found for sensitivities between 3 and 3.5 K. This range is narrower than most probabilistic estimates derived from the observed twentieth-century warming. The current generation of general circulation models are within that range but do not sample the highest values.

Palaeo-climate reconstruction from stable isotope variations in speleothems: a review

Speleothems are now regarded as valuable archives of climatic conditions on the continents, offering a number of advantages relative to other continental climate proxy recorders such as lake sediments and peat cores. They are ideal materials for precise U-series dating, yielding ages in calendar years, thereby circumventing the radiocarbon calibration problems associated with most other continental records. Stable isotope studies in speleothems have shifted away from attempting to provide palaeo-temperature reconstructions to the attainable goal of providing precise estimates for the timing and duration of major O isotope-defined climatic events characterised by high signal to noise ratios (e.g. glacial/interglacial transitions, Dansgaard–Oeschger oscillations, the ‘8200-year’ event). Unlike the marine records, speleothem data sets are not ‘tuned’, and their independent chronology offers opportunities to critically assess leads and lags in the climate system, that in turn can provide important insights into forcing and feedback mechanisms. Improved procedures for the extraction and measurement of stable isotope ratios in fluid inclusions trapped in speleothems are likely to provide, in the near future, a much enhanced basis for the quantitative interpretation of O isotope ratios in speleothem calcite. The latter developments open up once again the tantalising prospect of palaeo-temperature estimates, but more importantly perhaps, provide a direct test for a new generation of general circulation models whose hydrological cycles will incorporate the ‘water isotopes’. The literature is reviewed briefly to provide for the reader a sense of the current state-of-the-art, and to provide some pointers for future research directions.

A GCM-based assessment of the global moisture budget and the role of land-surface moisture reservoirs in processing precipitation

The performance of the Canadian Land Surface Scheme (CLASS) when coupled to the CCCma third generation general circulation model is evaluated in an AMIP II simulation. Our primary aim is to understand how CLASS processes moisture and to compare model estimates of moisture budget components with observations. The modelled mean annual precipitation and runoff, and their latitudinal structures, compare well with observations although some discrepancies remain in the simulation of regional values of these quantities. The amplitude and phase of the first harmonic of the precipitation annual cycle also compares well with observations although less well over regions of sparse precipitation and/or high topography. In the model, the canopy plays a major role in processing moisture at the land surface indicating the importance of vegetation in climate. The canopy intercepts a large fraction of the precipitation and provides the medium for returning much moisture back to the atmosphere as evapotranspiration. Though important locally, the snow moisture reservoir plays a relatively minor role in the global moisture budget. It acts primarily as a storage and delay mechanism with winter precipitation released to the ground reservoir on melting. The ground moisture reservoir also plays a major role and processes a similar amount of moisture as the canopy, although in a different manner. The globally averaged model runoff compares well with observation-based estimates, although the model partitioning into surface runoff and drainage does not agree particularly well with the single available observation-based estimate. How moisture is processed at the land surface serves as a basis for model intercomparison and for understanding the modelled moisture budget and its variation and changes with climate change. Only the most basic quantities (precipitation, runoff, and partitioning of runoff into surface runoff and drainage) may be compared with observation-based estimates, however, and the establishment of more complete moisture budget remains an important need.

Paradigm shifts in late-Holocene climatology?

The climatic consequences of major increases in the concentration of atmospheric carbon dioxide were calculated over 100 years ago, but only during the last two decades have the effects of human-induced increases in various atmospheric gas concentrations become a concern of a wide range of scientists. The near-century delay between the propounding of the theory and the widespread recognition of the consequences for climate of continuing human activities implies a recent shift in either the perspectives or in the interests of researchers. Here we use citation indices to discern a major shift in the focus of research into climatic change. Scientific findings in the 1970s and early 1980s are identified as a trigger to the development of wider scientific concern over human-induced climatic warming, while the period from 1988 to 1991 at first sight appears to represent a major paradigm shift. However, it is suggested here that an inferred change in scientific emphasis was caused primarily by a combination of (1) new evidence from ocean and ice cores, particularly concerning the relationship between past atmospheric gas concentrations and climatic change; (2) the availability and application of new tools, notably a new generation of General Circulation Models (GCMs); (3) attribution of human causation for other environmental problems; (4) a changing science research agenda, driven by political and funding considerations; and (5) the contemporary recording of apparently increased 'global' temperatures, which reversed a previously recorded cooling trend. We caution that the pre-eminence and longevity of the 'global warming' thesis is vulnerable either to meteorological data that do not fit with model scenarios, or to the rise or resurrection of other notions on the primary forcing factors in climatic change. To obtain a clear perspective on late-Holocene climatic change, it will be necessary to evaluate palaeoclimate data that derive from a wide range of complementary sources-sedimentological, biological, archaeological and documentary-and to compare the magnitude, rate and frequency of past climatic changes implied in those data with recorded twentieth-century 'global' changes and projected twenty-first-century scenarios.

Precipitation Scenarios over Iberia: A Comparison between Direct GCM Output and Different Downscaling Techniques

Abstract The Iberian rainfall regime is characterized by a strong seasonal cycle and large interannual variability. Typically, frequency distributions of monthly precipitation present a large spread of values, implying frequent episodes of very wet or very dry years. Unfortunately, the most recent generation of general circulation models (GCMs) still has serious problems when modeling monthly precipitation over southern Europe. However, these models are able to reproduce the main patterns of atmospheric circulation, such as those derived from a principal component analysis of the sea level pressure anomaly field. Many downscaling techniques have been developed in recent years, all having in common the need to establish statistical links between the large-scale circulation and the observed precipitation at a local or regional scale. The final objective is usually the application of such transfer functions to GCM output. In this work, linear and nonlinear downscaling transfer functions are developed based o...

Skill of seasonal hindcasts as a function of the ensemble size

Forecast skill as a function of the ensemble size is examined in a 24-member ensemble of northern winter (DJF) hindcasts produced with the second generation general circulation model of the Canadian Centre for Climate Modelling and Analysis. These integrations are initialized from the NCEP reanalyses at 6 h intervals prior to the forecast season. The sea surface temperatures that are applied as lower boundary conditions are predicted by persisting the monthly mean anomaly observed prior to the forecast period. The potential predictability that is attributed to lower boundary forced variability is estimated. In lagged-average forecasting, the forecast skill in the first two weeks, which originates predominately from the initial conditions, is greatest for relatively small ensemble sizes. The forecast skill increases monotonically with the ensemble size in the rest of the season. The skill of DJF 500 hPa geopotential height hindcasts in the Northern Hemisphere and in the Pacific/North America sector improves substantially when the ensemble size increases from 6 to 24. A statistical skill improvement technique based on the singular value decomposition method is also more successful for larger ensembles.

Using redundancy analysis to improve dynamical seasonal mean 500 hPa geopotential forecasts

AbstractIn this study, we evaluate and compare 500 hPa geopotential height hindcast skill in two large dynamical hindcast experiments performed with the Canadian Climate Centre second generation general circulation model (GCM). In one hindcast experiment, seasonal hindcasts are made from lagged initial conditions observed at the beginning of each season. The sea‐surface temperatures (SSTs) required by the model during each forecast period are forecast by persisting the SST anomalies observed during the month just prior to the forecast period. The second hindcast experiment consists of an ensemble of simulations in which continuously evolving observed SSTs are specified at the model's lower boundary. These hindcasts do not benefit from re‐specification of the initial state at the beginning of each season, but they do enjoy the benefit of ‘perfect’ SST forecasts. We also demonstrate the use of a regression technique, called redundancy analysis (RA), for statistically improving the skill of both types of dynamical hindcast. The results indicate that specification of the initial state at the beginning of each season adds skill to the seasonal hindcasts, even though SSTs at the lower boundary are imperfectly specified. We also find that the model can predict the mean state of the North Atlantic Oscillation (NAO) with some skill in boreal winter and spring when the initial state is specified at the beginning of each season. The results also indicate that statistical post‐processing with the RA technique improves the (cross‐validated) skill of both types of dynamical hindcast. Copyright © 2001 Royal Meteorological Society

Skill as a function of time scale in ensembles of seasonal hindcasts

Forecast skill as a function of time lead and time averaging is examined in two 6-member ensembles of seasonal hindcasts. One ensemble is produced with the second generation general circulation model of the Canadian Centre for Climate Modelling and Analysis (GCM2) and the other with a reduced resolution version of the numerical weather prediction model of the Canadian Meteorological Centre (SEF). The integrations are initiated from the NCEP/NCAR reanalyzed data. Monthly sea surface temperature anomalies observed prior to the forecast period are maintained throughout the forecast season. A statistical forecast improvement technique, based on the singular value decomposition of forecast and reanalyzed fields, is discussed and evaluated. A simple analogue of the hindcast integrations is used to examine the behavior of two common skill scores, the correlation skill score and the explained variance skill score. The maximal skill score and the corresponding optimal forecast in this analogue are identified. The total skill of the optimal forecast is a sum of two terms, one associated with the initial conditions and the other with the lower boundary forcing. The two sources of skill operate on different time scales, with initial conditions being more important in the first one-two weeks and the atmospheric response to the boundary forcing becoming more dominant for longer time leads and time averages. This suggests that these sources of skill should be considered separately in forecast optimization. The statistical technique is moderately successful in improving the skill of monthly to seasonal forecasts of 500 hPa height (Z500) and 700 hPa temperature (T700) in the Northern Hemisphere and in the North Pacific/North America sector. The improvement is better when the forecasts for the first week and for the rest of the season are optimized separately. The SEF model produces better Z500 and T700 forecasts than GCM2 in the first one-two weeks whereas GCM2 performs slightly better at longer time leads. The skill of zero time lead forecast decays rapidly with averaging interval for time averages up to about 30–45 days and stabilizes, or even rises, for longer time averages. Excluding the first week from seasonal forecasts results in substantial degradation of predictive skill.

Effects of specifying bottom boundary conditions in an ensemble of atmospheric GCM simulations

Interannual variability in an ensemble of six 47‐year climate simulations with specified time‐varying sea surface temperature (SST) and sea ice extent is analyzed. Variability is compared with that in a 250‐year control simulation with climatological SST and sea ice extent. The simulations were performed with the Canadian Climate Centre second generation General Circulation Model. An analysis of variance approach was combined with rotated Empirical Orthogonal Function analysis to assess the influence of the prescribed boundary conditions on the simulated atmospheric circulation structure and variability and to identify potentially predictable spatially coherent modes of variation. The quantities analyzed are seasonal mean 500 hPa geopotential (Z500) and mean sea level pressure (Pmsl). The prescribed boundary conditions increase the variability of the simulated atmosphere and modulate its large‐scale circulation structure. The effects on Z500 are found to be strongest in DJF and MAM and weakest in SON. The prescribed boundary conditions have a significant effect over the extratropical land areas, especially over northern North America, in El Niño‐Southern Oscillation (ENSO) years, but have little influence in non‐ENSO years. The variability of several of the leading spatial modes is significantly affected by the prescribed boundary conditions. In contrast with some previous studies, we find that the simulated North Atlantic Oscillation is not affected by the prescribed boundary forcing.