Abstract

A coarse resolution coupled ocean–atmosphere simulation in which surface albedo feedback is suppressed by prescribing surface albedo, is compared to one where snow and sea ice anomalies are allowed to affect surface albedo. Canonical CO₂-doubling experiments were performed with both models to assess the impact of this feedback on equilibrium response to external forcing. It accounts for about half the high-latitude response to the forcing. Both models were also run for 1000 yr without forcing to assess the impact of surface albedo feedback on internal variability. Surprisingly little internal variability can be attributed to this feedback, except in the Northern Hemisphere continents during spring and in the sea ice zone of the Southern Hemisphere year-round. At these locations and during these seasons, it accounts for, at most, 20% of the variability. The main reason for this relatively weak signal is that horizontal damping processes dilute the impact of surface albedo feedback.

When snow albedo feedback in Northern Hemisphere continents is isolated from horizontal damping processes, it has a similar strength in the CO₂-doubling and internal variability contexts; a given temperature anomaly in these regions is associated with approximately the same change in snow depth and surface albedo whether it was externally forced or internally generated. This suggests that the presence of internal variability in the observed record is not a barrier to extracting information about snow albedo feedback's contribution to equilibrium climate sensitivity. This is demonstrated in principle in a “scenario run,” where estimates of past, present, and future changes in greenhouse gases and sulfate aerosols are imposed on the model with surface albedo feedback. This simulation contains a mix of internal variations and externally forced anomalies similar to the observed record. The snow albedo feedback to the scenario run's climate anomalies agrees very well with the snow albedo feedback in the CO₂-doubling context. Moreover, the portion of the scenario run corresponding to the present-day satellite record is long enough to capture this feedback, suggesting this record could be used to estimate snow albedo feedback's contribution to equilibrium climate sensitivity.

Abstract

The strength of snow-albedo feedback (SAF) in transient climate change simulations of the Fourth Assessment of the Intergovernmental Panel on Climate Change is generally determined by the surface-albedo decrease associated with a loss of snow cover rather than the reduction in snow albedo due to snow metamorphosis in a warming climate. The large intermodel spread in SAF strength is likewise attributable mostly to the snow cover component. The spread in the strength of this component is in turn mostly attributable to a correspondingly large spread in mean effective snow albedo. Models with large effective snow albedos have a large surface-albedo contrast between snow-covered and snow-free regions and exhibit a correspondingly large surface-albedo decrease when snow cover decreases. Models without explicit treatment of the vegetation canopy in their surface-albedo calculations typically have high effective snow albedos and strong SAF, often stronger than observed. In models with explicit canopy treatment, completely snow-covered surfaces typically have lower albedos and the simulations have weaker SAF, generally weaker than observed. The authors speculate that in these models either snow albedos or canopy albedos when snow is present are too low, or vegetation shields snow-covered surfaces excessively. Detailed observations of surface albedo in a representative sampling of snow-covered surfaces would therefore be extremely useful in constraining these parameterizations and reducing SAF spread in the next generation of models.

Abstract

Climatological planetary albedo obtained from the International Satellite Cloud Climatology Project (ISCCP) D-series flux dataset is broken down into contributions from the surface and atmosphere in cryosphere regions. The atmosphere accounts for much more of climatological planetary albedo (≥75%) than the surface at all times of the year. The insignificance of the surface contribution over highly reflective cryosphere regions is attributed mostly to the damping effect of the atmosphere. The overlying atmosphere attenuates the surface’s contribution to climatological planetary albedo by reducing the number of solar photons initially reaching the surface and the number of photons initially reflected by the surface that actually reach the top of the atmosphere.

The ISCCP datasets were also used to determine the relative contributions of the surface and atmosphere to seasonal and interannual planetary albedo variability in cryosphere regions. Even damped by the atmosphere to the same degree as in the climatological case, the surface contribution dominates the variability in planetary albedo on seasonal and interannual time scales. The surface accounts for about 75% of the change in climatological planetary albedo from one season to another with similar zenith angle and more than 50% of its interannual variability at nearly all times of the year, especially during seasons with extensive snow and sea ice extent. The dominance of the surface in planetary albedo variability is because surface albedo variability associated with snow and ice fluctuations is significantly larger than atmospheric albedo variability due to cloud fluctuations. The large effect of snow and ice variations on planetary albedo variability suggests that if cloud fields do not change much in a future warmer climate, a retreat of snow cover or sea ice would lead to a significant increase in net incoming solar radiation, resulting in an enhancement of high-latitude climate sensitivity.

Abstract

Changes to mean and extreme wet season precipitation over California on interannual time scales are analyzed using twenty-first-century precipitation data from 34 global climate models. Models disagree on the sign of projected changes in mean precipitation, although in most models the change is very small compared to historical and simulated levels of interannual variability. For the 2020/21–2059/60 period, there is no projected increase in the frequency of extremely dry wet seasons in the ensemble mean. Wet extremes are found to increase to around 2 times the historical frequency, which is statistically significant at the 95% level. Stronger signals emerge in the 2060/61–2099/2100 period. Across all models, extremely dry wet seasons are roughly 1.5 to 2 times more common, and wet extremes generally triple in their historical frequency (statistically significant). Large increases in precipitation variability in most models account for the modest increases to dry extremes. Increases in the frequency of wet extremes can be ascribed to equal contributions from increased variability and increases to the mean. These increases in the frequency of interannual precipitation extremes will create severe water management problems in a region where coping with large interannual variability in precipitation is already a challenge. Evidence from models and observations is examined to understand the causes of the low precipitation associated with the 2013/14 drought in California. These lines of evidence all strongly indicate that the low 2013/14 wet season precipitation total can be very likely attributed to natural variability, in spite of the projected future changes in extremes.

Abstract

A study of the California Sierra Nevada snowpack has been conducted using snow station observations and reanalysis surface temperature data. Monthly snow water equivalent (SWE) measurements were combined from two datasets to provide sufficient data from 1930 to 2008. The monthly snapshots are used to calculate peak snow mass timing for each snow season. Since 1930, there has been an overall trend toward earlier snow mass peak timing by 0.6 days per decade. The trend toward earlier timing also occurs at nearly all individual stations. Even stations showing an increase in 1 April SWE exhibit the trend toward earlier timing, indicating that enhanced melting is occurring at nearly all stations. Analysis of individual years and stations reveals that warm daily maximum temperatures averaged over March and April are associated with earlier snow mass peak timing for all spatial and temporal scales included in the dataset. The influence is particularly pronounced for low accumulation years indicating the potential importance of albedo feedback for the melting of shallow snow. The robustness of the early spring temperature influence on peak timing suggests the trend toward earlier peak timing is attributable to the simultaneous warming trend (0.1°C decade⁻¹ since 1930, with an acceleration in warming in later time periods). Given future scenarios of warming in California, one can expect acceleration in the trend toward earlier peak timing; this will reduce the warm season storage capacity of the California snowpack.

Abstract

Zonally symmetric fluctuations of the midlatitude westerly winds characterize the primary mode of atmospheric variability in the Southern Hemisphere during all seasons. This is true not only in observations but also in an unforced 15 000-yr integration of a coarse-resolution (R15) coupled ocean–atmosphere model. Here it is documented how this mode of atmospheric variability, known as the Southern Annular Mode (SAM), generates ocean circulation and sea ice variations in the model integration on interannual to centennial timescales that are tightly in phase with the SAM. The positive phase of the SAM is associated with an intensification of the surface westerlies over the circumpolar ocean (around 60°S), and a weakening of the surface westerlies farther north. This induces Ekman drift to the north at all longitudes of the circumpolar ocean, and Ekman drift to the south at around 30°S. Through mass continuity, the Ekman drift generates anomalous upwelling along the margins of the Antarctic continent, and downwelling around 45°S. The anomalous flow diverging from the Antarctic continent also increases the vertical tilt of the isopycnals in the Southern Ocean, so that a more intense circumpolar current is also closely associated with positive SAM. In addition, the anomalous divergent flow advects sea ice farther north, resulting in an increase in sea ice coverage. Finally, positive SAM drives increases in poleward heat transport at about 30°S, while decreases occur in the circumpolar region. Ocean and sea ice anomalies of the opposite sign occur when the SAM is negative. The ocean and sea ice fluctuations associated with the SAM constitute a significant fraction of simulated ocean variability poleward of 30°S year-round. The robustness of the mechanisms relating the SAM to oceanic variability suggests that the SAM is likely an important source of large-scale variability in the real Southern Hemisphere ocean.

Abstract

In this paper, the two factors controlling Northern Hemisphere springtime snow albedo feedback in transient climate change are isolated and quantified based on scenario runs of 17 climate models used in the Intergovernmental Panel on Climate Change Fourth Assessment Report. The first factor is the dependence of planetary albedo on surface albedo, representing the atmosphere's attenuation effect on surface albedo anomalies. It is potentially a major source of divergence in simulations of snow albedo feedback because of large differences in simulated cloud fields in Northern Hemisphere land areas. To calculate the dependence, an analytical model governing planetary albedo was developed. Detailed validations of the analytical model for two of the simulations are shown, version 3 of the Community Climate System Model (CCSM3) and the Geophysical Fluid Dynamics Laboratory global coupled Climate Model 2.0 (CM2.0), demonstrating that it facilitates a highly accurate calculation of the dependence of planetary albedo on surface albedo given readily available simulation output. In all simulations it is found that surface albedo anomalies are attenuated by approximately half in Northern Hemisphere land areas as they are transformed into planetary albedo anomalies. The intermodel standard deviation in the dependence of planetary albedo on surface albedo is surprisingly small, less than 10% of the mean. Moreover, when an observational estimate of this factor is calculated by applying the same method to the satellite-based International Satellite Cloud Climatology Project (ISCCP) data, it is found that most simulations agree with ISCCP values to within about 10%, despite further disagreements between observed and simulated cloud fields. This suggests that even large relative errors in simulated cloud fields do not result in significant error in this factor, enhancing confidence in climate models. The second factor, related exclusively to surface processes, is the change in surface albedo associated with an anthropogenically induced temperature change in Northern Hemisphere land areas. It exhibits much more intermodel variability. The standard deviation is about ⅓ of the mean, with the largest value being approximately 3 times larger than the smallest. Therefore this factor is unquestionably the main source of the large divergence in simulations of snow albedo feedback. To reduce the divergence, attention should be focused on differing parameterizations of snow processes, rather than intermodel variations in the attenuation effect of the atmosphere on surface albedo anomalies.

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.

Abstract

High-resolution gridded datasets are in high demand because they are spatially complete and include important finescale details. Previous assessments have been limited to two to three gridded datasets or analyzed the datasets only at the station locations. Here, eight high-resolution gridded temperature datasets are assessed two ways: at the stations, by comparing with Global Historical Climatology Network–Daily data; and away from the stations, using physical principles. This assessment includes six station-based datasets, one interpolated reanalysis, and one dynamically downscaled reanalysis. California is used as a test domain because of its complex terrain and coastlines, features known to differentiate gridded datasets. As expected, climatologies of station-based datasets agree closely with station data. However, away from stations, spread in climatologies can exceed 6°C. Some station-based datasets are very likely biased near the coast and in complex terrain, due to inaccurate lapse rates. Many station-based datasets have large unphysical trends (>1°C decade⁻¹) due to unhomogenized or missing station data—an issue that has been fixed in some datasets by using homogenization algorithms. Meanwhile, reanalysis-based gridded datasets have systematic biases relative to station data. Dynamically downscaled reanalysis has smaller biases than interpolated reanalysis, and has more realistic variability and trends. Dynamical downscaling also captures snow–albedo feedback, which station-based datasets miss. Overall, these results indicate that 1) gridded dataset choice can be a substantial source of uncertainty, and 2) some datasets are better suited for certain applications.