Christopher G. Fletcher

The CCSM4 land simulation, 1850-2005: Assessment of surface climate and new capabilities

AbstractThis paper reviews developments for the Community Land Model, version 4 (CLM4), examines the land surface climate simulation of the Community Climate System Model, version 4 (CCSM4) compared to CCSM3, and assesses new earth system features of CLM4 within CCSM4. CLM4 incorporates a broad set of improvements including additions of a carbon–nitrogen (CN) biogeochemical model, an urban canyon model, and transient land cover and land use change, as well as revised soil and snow submodels.Several aspects of the surface climate simulation are improved in CCSM4. Improvements in the simulation of soil water storage, evapotranspiration, surface albedo, and permafrost that are apparent in offline CLM4 simulations are generally retained in CCSM4. The global land air temperature bias is reduced and the annual cycle is improved in many locations, especially at high latitudes. The global land precipitation bias is larger in CCSM4 because of bigger wet biases in central and southern Africa and Australia.New earth...

The Dynamical Response to Snow Cover Perturbations in a Large Ensemble of Atmospheric GCM Integrations

Variability in the extent of fall season snow cover over the Eurasian sector has been linked in observations to a teleconnection with the winter northern annular mode pattern. Here, the dynamics of this teleconnection are investigated using a 100-member ensemble of transient integrations of the GFDL atmospheric general circulation model (AM2). The model is perturbed with a simple persisted snow anomaly over Siberia and is integrated from October through December. Strong surface cooling occurs above the anomalous Siberian snow cover, which produces a tropospheric form stress anomaly associated with the vertical propagation of wave activity. This wave activity response drives wave‐mean flow interaction in the lower stratosphere and subsequent downward propagation of a negative-phase northern annular mode response back into the troposphere. A wintertime coupled stratosphere‐troposphere response to fall season snow forcing is also found to occur even when the snow forcing itself does not persist into winter. Finally, the response to snow forcing is compared in versions of the same model with and without a well-resolved stratosphere. The version with the well-resolved stratosphere exhibits a faster and weaker response to snow forcing, and this difference is tied to the unrealistic representation of the unforced lower-stratospheric circulation in that model.

Improved Skill of Northern Hemisphere Winter Surface Temperature Predictions Based on Land–Atmosphere Fall Anomalies

Abstract A statistical forecast model, referred to as the snow-cast (sCast) model, has been developed using observed October mean snow cover and sea level pressure anomalies to predict upcoming winter land surface temperatures for the extratropical Northern Hemisphere. In operational forecasts since 1999, snow cover has been used for seven winters, and sea level pressure anomalies for three winters. Presented are skill scores for these seven real-time forecasts and also for 33 winter hindcasts (1972/73–2004/05). The model demonstrates positive skill over much of the eastern United States and northern Eurasia—regions that have eluded skillful predictions among the existing major seasonal forecast centers. Comparison with three leading dynamical forecast systems shows that the statistical model produces superior skill for the same regions. Despite the increasing complexity of the dynamical models, they continue to derive their forecast skill predominantly from tropical atmosphere–ocean coupling, in particul...

The Role of Linear Interference in the Annular Mode Response to Tropical SST Forcing

Abstract Recent observational and modeling studies have demonstrated a link between eastern tropical Pacific Ocean (TPO) warming associated with the El Nino–Southern Oscillation (ENSO) and the negative phase of the wintertime northern annular mode (NAM). The TPO–NAM link involves a Rossby wave teleconnection from the tropics to the extratropics, and an increase in polar stratospheric wave driving that in turn induces a negative NAM anomaly in the stratosphere and troposphere. Previous work further suggests that tropical Indian Ocean (TIO) warming is associated with a positive NAM anomaly, which is of opposite sign to the TPO case. The TIO case is, however, difficult to interpret because the TPO and TIO warmings are not independent. To better understand the dynamics of tropical influences on the NAM, the current study investigates the NAM response to imposed TPO and TIO warmings in a general circulation model. The NAM responses to the two warmings have opposite sign and can be of surprisingly similar ampli...

The Role of Linear Interference in the Annular Mode Response to Extratropical Surface Forcing

Abstract The classical problem of predicting the atmospheric circulation response to extratropical surface forcing is revisited in the context of the observed connection between autumnal snow cover anomalies over Siberia and wintertime anomalies of the northern annular mode (NAM). Previous work has shown that in general circulation model (GCM) simulations in which autumnal Siberian snow forcing is prescribed, a vertically propagating Rossby wave train is generated that propagates into the stratosphere, drives dynamical stratospheric warming, and induces a negative NAM response that couples to the troposphere. Important questions remain regarding the dynamics of the response to this surface cooling. It is shown that previously unexplained aspects of the evolution of the response in a comprehensive GCM can be explained by examining the time evolution of the phasing, and hence the linear interference, between the Rossby wave response and the background climatological stationary wave. When the wave response a...

Quantifying the skill of CMIP5 models in simulating seasonal albedo and snow cover evolution

Effectively modeling the influence of terrestrial snow on climate in general circulation models is limited by imperfect knowledge and parameterization of arctic and subarctic climate processes and a lack of reliable observations for model evaluation and improvement. This study uses a number of satellite-derived data sets to evaluate how well the current generation of climate models from the Fifth Coupled Model Intercomparison Project (CMIP5) simulate the seasonal cycle of climatological snow cover fraction (SCF) and surface albedo over the Northern Hemisphere snow season (September–June). Using a variety of metrics, the CMIP5 models are found to simulate SCF evolution better than that of albedo. The seasonal cycle of SCF is well reproduced despite substantial biases in simulated surface albedo of snow-covered land (αsfc_snow), which affect both the magnitude and timing of the seasonal peak in αsfc_snow during the fall snow accumulation period, and the springtime snow ablation period. Insolation weighting demonstrates that the biases in αsfc_snow during spring are of greater importance for the surface energy budget. Albedo biases are largest across the boreal forest, where the simulated seasonal cycle of albedo is biased high in 15/16 CMIP5 models. This bias is explained primarily by unrealistic treatment of vegetation masking and subsequent overestimation (more than 50% in some cases) of peak αsfc_snow rather than by biases in SCF. While seemingly straightforward corrections to peak αsfc_snow could yield significant improvements to simulated snow albedo feedback, changes in αsfc_snow could potentially introduce biases in other important model variables such as surface temperature.

The influence of canopy snow parameterizations on snow albedo feedback in boreal forest regions

Variation in snow albedo feedback (SAF) among Coupled Model Intercomparison Project phase 5 climate models has been shown to explain much of the variation in projected 21st century warming over Northern Hemisphere land. Prior studies using observations and models have demonstrated both considerable spread in the albedo and a negative bias in the simulated strength of SAF, over snow-covered boreal forests. Boreal evergreen needleleaf forests are capable of intercepting snowfall throughout the winter and consequently exert a significant impact on seasonal surface albedo. Two satellite data products and tower-based observations of albedo are compared with simulations from multiple versions of the Community Climate System Model (CCSM4) to investigate the causes of weak simulated SAF over the boreal forest. The largest bias occurs in April and May, when simulated SAF is one half the strength of SAF in observations. This is traced to two features of the canopy snow parameterizations used in the land model. First, there is no mechanism for the dynamic removal of snow from the canopy when temperatures are below freezing, which results in albedo values in midwinter that are biased high. Second, when temperatures do rise above freezing, all snow on the canopy is melted instantaneously, which results in an unrealistically early transition from a snow-covered to a snow-free canopy. These processes combine to produce large differences between simulated and observed monthly albedo and are the source of the weak bias in SAF. This analysis highlights the importance of canopy snow parameterizations for simulating the hemispheric scale climate response to surface albedo perturbations.

Evaluating biases in simulated snow albedo feedback in two generations of climate models

This study presents a comprehensive evaluation of snow albedo feedback (SAF) in two generations of climate models (Coupled Model Intercomparison Project versions 3 (CMIP3) and 5 (CMIP5)). A comparison of the models is performed against a multiobservation-based reference data set (mOBS) derived from the seasonal cycle of albedo, snow cover, and temperature. The observed total SAF shows low uncertainty and is generally well simulated by the CMIP3 and CMIP5 ensemble mean, except for a low (high) bias over the Arctic (northern boreal forest). Most CMIP5 models overestimate the snow cover component of SAF (SNC) and underestimate the temperature sensitivity component (TEM). The high bias in SNC is due to simulated snow albedos 4–5% brighter than observed driving unrealistically large albedo contrasts. However, overall representation of surface albedo—and mean climate—has improved, as fewer CMIP5 models exhibit large cold temperature, or high snow, biases. The low bias in TEM is related to overly persistent snow albedo during spring, particularly over southern Eurasia and North America. There is large observational uncertainty in the reference data set mOBS that is traced primarily to the different snow cover products, with a secondary contribution from the albedo products and a small contribution from the temperature products. The conclusion is that the model mean tends to simulate the multiobservation mean very closely; however, this masks considerable spread in both models and observations. There is clear motivation for producing improved submonthly snow cover products for the purpose of model evaluation.

Winter North Atlantic Oscillation Hindcast Skill: 1900–2001

Abstract Recent proposed seasonal hindcast skill estimates for the winter North Atlantic Oscillation (NAO) are derived from different lagged predictors, NAO indices, skill assessment periods, and skill validation methodologies. This creates confusion concerning what is the best-lagged predictor of the winter NAO. To rectify this situation, a standardized comparison of NAO cross-validated hindcast skill is performed against three NAO indices over three extended periods (1900–2001, 1950–2001, and 1972–2001). The lagged predictors comprise four previously published predictors involving anomalies in North Atlantic sea surface temperature (SST), Northern Hemisphere (NH) snow cover, and an additional predictor, an index of NH subpolar summer air temperature (TSP). Significant (p < 0.05) NAO hindcast skill is found with May SST 1900–2001, summer/autumn SST 1950–2001, and warm season snow cover 1972–2001. However, the highest and most significant hindcast skill for all periods and all NAO indices is achieved with...

Snow albedo feedback Current knowledge, importance, outstanding issues and future directions

Over the past decade, substantial progress has been made in improving our understanding of surface albedo feedbacks, where changes in surface albedo from warming (cooling) can cause increases (decreases) in absorbed solar radiation, amplifying the initial warming (cooling). The goal of this review is to synthesize and assess recent research into the feedback caused by changing continental snow cover, or snow albedo feedback (SAF). Four main topics are evaluated: (i) the importance of SAF to the global energy budget, (ii) estimates of SAF from various data sources, (iii) factors influencing the spread in SAF, and (iv) outstanding issues related to our understanding of the physical processes that control SAF (and their uncertainties). SAF is found to exert a small influence on a global scale, with amplitude of ∼ 0.1 Wm−2 K−1, roughly 7% of the strength of water vapor feedback. However, SAF is an important driver of regional climate change over Northern Hemisphere (NH) extratropical land, where observation-based estimates show a peak feedback of around 1% decrease in surface albedo per degree of warming during spring. Viewed collectively, the current generation of climate models represent this process accurately, but several models still use outdated parameterizations of snow and surface albedo that contribute to biases that impact the simulation of SAF. This discussion serves to synthesize and evaluate previously published literature, while highlighting promising directions being taken at the forefront of research such as high resolution modeling and the use of large ensembles.