NCAR Community Climate System Model Research Articles

CWRF downscaling and understanding of China precipitation projections

The regional Climate-Weather Research and Forecasting model (CWRF) was used to downscale the NCAR Community Climate System Model V4.0 (CCSM4) projection of China precipitation changes from the present (1974–2005) to future (2019–2050) under the high emission scenario RCP8.5. The CWRF downscaling at 30-km improved CCSM4 in capturing observed key precipitation spatiotemporal characteristics, correcting rainband dislocations, seasonal-mean biases, extreme-rainfall underestimates and rainy-day overestimates. For the future, CWRF generally reduced CCSM4 projected changes in magnitude, producing still significant increases mostly in summer for mean precipitation in the Northeast, North China and Southwest and for extreme precipitation in North China, South China and the Southwest. These regional precipitation increases were direct responses to enhanced ascending motions and moisture transports from adjacent oceans as the east Asian jet shrunk westward and the Hadley circulation widened northward under global warming. The identification of such robust physical mechanisms added confidence in the CWRF downscaled regional precipitation changes. Furthermore, the CWRF downscaling corrections were systematically carried from the present into future, accounting for projection uncertainties up to 40%. Regional biases, however, could not be simply removed from projected changes because their correspondences were strongly nonlinear, highlighting CWRF’s ability to project more reliable changes by reducing model structural uncertainties.

Dynamical Downscaling for Southeast Alaska: Historical Climate and Future Projections

AbstractWarming temperatures across southeast Alaska are affecting the region’s energy and transportation sectors, marine ecosystems, and forest health. More frequent above-freezing temperatures lead a transition from snow- to rain-dominant precipitation regimes, accelerating glacial mass balance loss and a leading to a greater risk for warm-season drought. Southeast Alaska has steep topographical gradients, which necessitate the use of downscaled climate information to study historical and projected periods. This study used regional dynamical downscaling at 4-km spatial resolution with the Weather Research and Forecasting Model to assess historical (1981–2010) and projected (2031–60) climate states for southeast Alaska. These simulations were driven by one reanalysis (i.e., the Climate Forecast System Reanalysis) and two climate models (i.e., the Geophysical Fluid Dynamics Laboratory Climate Model, version 3, and the NCAR Community Climate System Model, version 4), which each included a historical simulation and a projected simulation. The future simulations used the representative concentration pathway 8.5 emissions scenario. Bias-corrected projections (2031–60 minus 1981–2010) indicated seasonal warming of 1°–3°C, increased precipitation during autumn (4%–12%) and winter (7%–12%), and decreased snowfall in all seasons (up to 60% in autumn). The average number of days annually with a minimum temperature below freezing dropped by more than 30. The average annual maximum consecutive 3-day precipitation amounts increased by 11%–16%, but analogous extreme snowfall amounts dropped by 5%–11%. The most substantial snow losses occurred at low-elevation and coastal locations; at many high elevations (e.g., above 1000 m), extreme snowfall amounts increased.

Agro-Climate Projections for a Warming Alaska

Abstract Climate warming is expected to disproportionately affect crop yields in the southern United States due to excessive heat stress, while presenting new farming opportunities through a longer growing season farther north. Few studies have investigated the impact of this warming on agro-climate indices that link meteorological data with important field dates in northern regions. This study employs regional dynamical downscaling using the Weather Research and Forecasting (WRF) Model to assess changes in growing season length (GSL), spring planting dates, and occurrences of plant heat stress (PHS) for five regions in Alaska. Differences between future representative concentration pathway 8.5 (RCP8.5; 2011–40, 2041–70, 2071–2100) and historical (1981–2010) periods are obtained using boundary forcing from the Geophysical Fluid Dynamics Laboratory Climate Model, version 3, and the NCAR Community Climate System Model, version 4. The model output is bias corrected using ERA-Interim. Median GSL shows increases of 48–87 days by 2071–2100, with the largest changes in northern Alaska. Similarly, by 2071–2100, planting dates advance 2–4 weeks, and PHS days increase from near 0 to 5–10 instances per summer in the hottest areas. The largest GSL changes occur in the mid- (2041–70) and late century (2071–2100), when a warming signal emerges from the historical interannual variability. These periods coincide with the greatest divergence of the RCPs, suggesting that near-term decision-making may affect substantial future changes. Early-century (2011–40) projections show median GSL increases of 8–27 days, which is close to the historical standard deviation of GSL. Thus, internal variability will remain an important source of uncertainty into the midcentury, despite a trend for longer growing seasons.

An examination of the Northern Hemisphere mid-latitude storm track interannual variability simulated by climate models\u2014sensitivity to model resolution and coupling

The model fidelity in simulating the Northern Hemisphere storm track interannual variability and the connections of this variability to the low frequency atmospheric variations and oceanic variations are examined based on the atmospheric European Centre for Medium-Range Weather Forecasts (ECMWF) model and coupled NCAR Community Climate System Model (CCSM) systems at different horizontal resolutions. The atmospheric general circulation model (AGCM) runs are forced by observed sea surface temperatures (SST) with varying atmospheric resolutions, while the coupled general circulation model (CGCM) runs have a fixed atmospheric resolution but varying oceanic resolutions. The phases, between the North Pacific (NP) and North Atlantic (NA) sectors, of the simulated hemisphere-scale Empirical Orthogonal Function (EOF) modes of the storm track fluctuations change with the model resolution, suggesting the storm track variability in NP and NA basins are largely independent. The models can qualitatively reproduce the basin-scale EOFs of both NP and NA storm track variability. These EOFs are not sensitive to either atmospheric or oceanic model horizontal resolutions, but their magnitudes from the CGCM runs are substantially underestimated. The storm track variations over NP basin are hybrid of internal atmospheric variations and external forcing from the underlying conditions, but the fluctuations over the NA basin are merely atmospheric internal variability. The NP storm track variability from SST forcing accounts for 4.4% of the total variance in observations, while it only has less than 2% of the total in all AGCM simulations. The external forcing to the storm track variations is more realistically reproduced in the higher atmospheric resolution runs. The air–sea coupling makes the SST feedbacks to the atmospheric internal variability, absent in the atmospheric ECMWF model hindcasts, emerge in the coupled CCSM simulations.

Trends of Climate Change in Saudi Arabia: Implications on Water Resources

Climate change is an important factor for sustainable water resource management in the arid and semi-arid countries. In this study, future trends of temperature and rainfall were assessed for several regions in Saudi Arabia. The linear and Mann–Kendall analyses showed an increase of temperature in all regions and decrease of rainfall in many regions. Following trend analysis, the outputs of the NCAR Community Climate System Model were obtained for three emission scenarios (high: representative concentration pathways RCP8.5; high medium: RCP6; and low: RCP2.6) for the assessment periods of 2025–2044, 2045–2064 and 2065–2084 respectively, and compared with the average values from the reference period (1986–2005). In all emission scenarios, temperature showed an increase from 1986 to 2005 in all regions. For RCP8.5, increase of temperature are in the ranges of 0.8–1.6 °C, 0.9–2.7 °C and 0.7–4.1 °C during 2025–2044, 2045–2064 and 2065–2084 respectively. However, rainfall showed variable patterns with respect to emission scenarios and assessment periods. In most regions, the RCP6 showed decrease in rainfall from the reference period while the RCP8.5 and RCP2.6 showed variable patterns. The increase of temperature and variable pattern of rainfall may increase uncertainty in developing sustainable water resource management strategies.

Wintertime Atmospheric Response to North Atlantic Ocean Circulation Variability in a Climate Model

Abstract Maximum covariance analysis of a preindustrial control simulation of the NCAR Community Climate System Model, version 4 (CCSM4), shows that a barotropic signal in winter broadly resembling a negative phase of the North Atlantic Oscillation (NAO) follows an intensification of the Atlantic meridional overturning circulation (AMOC) by about 7 yr. The delay is due to the cyclonic propagation along the North Atlantic Current (NAC) and the subpolar gyre of a SST warming linked to a northward shift and intensification of the NAC, together with an increasing SST cooling linked to increasing southward advection of subpolar water along the western boundary and a southward shift of the Gulf Stream (GS). These changes result in a meridional SST dipole, which follows the AMOC intensification after 6 or 7 yr. The SST changes were initiated by the strengthening of the western subpolar gyre and by bottom torque at the crossover of the deep branches of the AMOC with the NAC on the western flank of the Mid-Atlantic Ridge and the GS near the Tail of the Grand Banks, respectively. The heat flux damping of the SST dipole shifts the region of maximum atmospheric transient eddy growth southward, leading to a negative NAO-like response. No significant atmospheric response is found to the Atlantic multidecadal oscillation (AMO), which is broadly realistic but shifted south and associated with a much weaker meridional SST gradient than the AMOC fingerprint. Nonetheless, the wintertime atmospheric response to the AMOC shows some similarity with the observed response to the AMO, suggesting that the ocean–atmosphere interactions are broadly realistic in CCSM4.

Role of Climate Feedback in El Niño–Like SST Response to Global Warming

Abstract Under global warming from the doubling of CO2, the equatorial Pacific experiences an El Niño–like warming, as simulated by most global climate models. A new climate feedback and response analysis method (CFRAM) is applied to 10 years of hourly output of the slab ocean model (SOM) version of the NCAR Community Climate System Model, version 3.0, (CCSM3-SOM) to determine the processes responsible for this warming. Unlike the traditional surface heat budget analysis, the CFRAM can explicitly quantify the contributions of each radiative climate feedback and of each physical and dynamical process of a GCM to temperature changes. The mean bias in the sum of partial SST changes due to each feedback derived with CFRAM in the tropical Pacific is negligible (0.5%) compared to the mean SST change from the CCSM3-SOM simulations, with a spatial pattern correlation of 0.97 between the two. The analysis shows that the factors contributing to the El Niño–like SST warming in the central Pacific are different from those in the eastern Pacific. In the central Pacific, the largest contributor to El Niño–like SST warming is dynamical advection, followed by PBL diffusion, water vapor feedback, and surface evaporation. In contrast, in the eastern Pacific the dominant contributor to El Niño–like SST warming is cloud feedback, with water vapor feedback further amplifying the warming.

Individual Feedback Contributions to the Seasonality of Surface Warming

Abstract Using the climate feedback response analysis method, the authors examine the individual contributions of the CO2 radiative forcing and climate feedbacks to the magnitude, spatial pattern, and seasonality of the transient surface warming response in a 1% yr−1 CO2 increase simulation of the NCAR Community Climate System Model, version 4 (CCSM4). The CO2 forcing and water vapor feedback warm the surface everywhere throughout the year. The tropical warming is predominantly caused by the CO2 forcing and water vapor feedback, while the evaporation feedback reduces the warming. Most feedbacks exhibit noticeable seasonal variations; however, their net effect has little seasonal variation due to compensating effects, which keeps the tropical warming relatively invariant all year long. The polar warming has a pronounced seasonal cycle, with maximum warming in fall/winter and minimum warming in summer. In summer, the large cancelations between the shortwave and longwave cloud feedbacks and between the surface albedo feedback warming and the cooling from the ocean heat storage/dynamics feedback lead to a warming minimum. In polar winter, surface albedo and shortwave cloud feedbacks are nearly absent due to a lack of insolation. However, the ocean heat storage feedback relays the polar warming due to the surface albedo feedback from summer to winter, and the longwave cloud feedback warms the polar surface. Therefore, the seasonal variations in the cloud feedback, surface albedo feedback, and ocean heat storage/dynamics feedback, directly caused by the strong annual cycle of insolation, contribute primarily to the large seasonal variation of polar warming. Furthermore, the CO2 forcing and water vapor and atmospheric dynamics feedbacks add to the maximum polar warming in fall/winter.

Climate Change Impacts on the Water Balance of the Colorado Headwaters: High-Resolution Regional Climate Model Simulations

Abstract A high-resolution climate model (4-km horizontal grid spacing) is used to examine the following question: How will long-term changes in climate impact the partitioning of annual precipitation between evapotranspiration and runoff in the Colorado Headwaters? This question is examined using a climate sensitivity approach in which eight years of current climate is compared to a future climate created by modifying the current climate signal with perturbation from the NCAR Community Climate System Model, version 3 (CCSM3), model forced by the A1B scenario for greenhouse gases out to 2050. The current climate period is shown to agree well with Snowpack Telemetry (SNOTEL) surface observations of precipitation (P) and snowpack, as well as streamflow and AmeriFlux evapotranspiration (ET) observations. The results show that the annual evaporative fraction (ET/P) for the Colorado Headwaters is 0.81 for the current climate and 0.83 for the future climate, indicating increasing aridity in the future despite a positive increase of precipitation. Runoff decreased by an average of 6%, reflecting the increased aridity. Precipitation increased in the future winter by 12%, but decreased in the summer as a result of increased low-level inhibition to convection. The fraction of precipitation that fell as snow decreased from 0.83 in the current climate to 0.74 in the future. Future snowpack did not change significantly until January. From January to March the snowpack increased above ~3000 m MSL and decreased below that level. Snowpack decreased at all elevations in the future from April to July. The peak snowpack and runoff over the headwaters occurred 2–3 weeks earlier in the future simulation, in agreement with previous studies.

Characterizing the Climate Feedback Pattern in the NCAR CCSM3-SOM Using Hourly Data

Abstract The climate feedback–response analysis method (CFRAM) was applied to 10-yr hourly output of the NCAR Community Climate System Model, version 3, using the slab ocean model (CCSM3-SOM), to analyze the strength and spatial distribution of climate feedbacks and to characterize their contributions to the global and regional surface temperature Ts changes in response to a doubling of CO2. The global mean bias in the sum of partial Ts changes associated with the CO2 forcing, and each feedback derived with the CFRAM analysis is about 2% of Ts change obtained directly from the CCSM3-SOM simulations. The pattern correlation between the two is 0.94, indicating that the CFRAM analysis using hourly model output is accurate and thus is appropriate for quantifying the contributions of climate feedback to the formation of global and regional warming patterns. For global mean Ts, the largest contributor to the warming is water vapor feedback, followed by the direct CO2 forcing and albedo feedback. The albedo feedback exhibits the largest spatial variation, followed by shortwave cloud feedback. In terms of pattern correlation and RMS difference with the modeled global surface warming, longwave cloud feedback contributes the most. On zonal average, albedo feedback is the largest contributor to the stronger warming in high latitudes than in the tropics. The longwave cloud feedback further amplifies the latitudinal warming contrast. Both the land–ocean warming difference and contributions of climate feedbacks to it vary with latitude. Equatorward of 50°, shortwave cloud feedback and dynamical advection are the two largest contributors. The land–ocean warming difference on the hemispheric scale is mainly attributable to longwave cloud feedback and convection.

Why is the AMOC Monostable in Coupled General Circulation Models?

Abstract This paper is concerned with the question: why do coupled general circulation models (CGCM) seem to be biased toward a monostable Atlantic meridional overturning circulation (AMOC)? In particular, the authors investigate whether the monostable behavior of the CGCMs is caused by a bias of model surface climatology. First observational literature is reviewed, and it is suggested that the AMOC is likely to be bistable in the real world in the past and present. Then the stability of the AMOC in the NCAR Community Climate System Model, version 3 (CCSM3) is studied by comparing the present-day control simulation (without flux adjustment) with a sensitivity experiment with flux adjustment. It is found that the monostable AMOC in the control simulation is altered to a bistable AMOC in the flux-adjustment experiment because a reduction of the surface salinity biases in the tropical and northern North Atlantic leads to a reduction of the bias of freshwater transport in the Atlantic. In particular, the tropical bias associated with the double ITCZ reduces salinity in the upper South Atlantic Ocean and, in turn, the AMOC freshwater export, which tends to overstabilize the AMOC and therefore biases the AMOC from bistable toward monostable state. This conclusion is consistent with a further analysis of the stability indicator of two groups of IPCC Fourth Assessment Report (AR4) CGCMs: one without and the other with flux adjustment. Because the tropical bias is a common feature among all CGCMs without flux adjustment, the authors propose that the surface climate bias, notably the tropical bias in the Atlantic, may contribute significantly to the monostability of AMOC behavior in current CGCMs.

A Note on the Stability Indicator of the Atlantic Meridional Overturning Circulation

Abstract This study examines the validity of the net freshwater transport ΔMov as a stability indicator of the Atlantic meridional overturning circulation (AMOC) in a low-resolution version of the NCAR Community Climate System Model, version 3 (CCSM3). It is shown that the sign of ΔMov indicates the monostability or bistability of the AMOC, which is based on a hypothesis that a collapsed AMOC induces a zero net freshwater transport. In CCSM3, this hypothesis is satisfied in that the collapsed AMOC, with a nonzero strength, induces a zero net freshwater transport ΔMov across the Atlantic basin by generating equivalent freshwater export MovS and freshwater import MovN at the southern and northern boundaries, respectively. Because of the satisfaction of the hypothesis, ΔMov is consistent with a generalized indicator L for a slowly evolving AMOC, both of which correctly monitor the AMOC stability.

The Influence of the AMOC Variability on the Atmosphere in CCSM3

Abstract The influence of the Atlantic meridional overturning circulation (AMOC) variability on the atmospheric circulation is investigated in a control simulation of the NCAR Community Climate System Model, version 3 (CCSM3), where the AMOC evolves from an oscillatory regime into a red noise regime. In the latter, an AMOC intensification is followed during winter by a positive North Atlantic Oscillation (NAO). The atmospheric response is robust and controlled by AMOC-driven SST anomalies, which shift the heat release to the atmosphere northward near the Gulf Stream/North Atlantic Current. This alters the low-level atmospheric baroclinicity and shifts the maximum eddy growth northward, affecting the storm track and favoring a positive NAO. The AMOC influence is detected in the relation between seasonal upper-ocean heat content or SST anomalies and winter sea level pressure. In the oscillatory regime, no direct AMOC influence is detected in winter. However, an upper-ocean heat content anomaly resembling the AMOC footprint precedes a negative NAO. This opposite NAO polarity seems due to the southward shift of the Gulf Stream during AMOC intensification, displacing the maximum baroclinicity southward near the jet exit. As the mode has somewhat different patterns when using SST, the wintertime impact of the AMOC lacks robustness in this regime. However, none of the signals compares well with the observed influence of North Atlantic SST anomalies on the NAO because SST is dominated in CCSM3 by the meridional shifts of the Gulf Stream/North Atlantic Current that covary with the AMOC. Hence, although there is some potential climate predictability in CCSM3, it is not realistic.

How Well Do Climate Models Reproduce North Atlantic Subtropical Mode Water?

Abstract Formation and the subsequent evolution of the subtropical mode water (STMW) involve various dynamic and thermodynamic processes. Proper representation of mode water variability and contributions from various processes in climate models is important in order to predict future climate change under changing forcings. The North Atlantic STMW, often referred to as Eighteen Degree Water (EDW), in three coupled models, both with data assimilation [GFDL coupled data assimilation (GFDL CDA)] and without data assimilation [GFDL Climate Model, version 2.1 (GFDL CM2.1), and NCAR Community Climate System Model, version 3 (CCSM3)], is analyzed to evaluate how well EDW processes are simulated in those models and to examine whether data assimilation alters the model response to forcing. In comparison with estimates from observations, the data-assimilating model gives a better representation of the formation rate, the spatial distribution of EDW, and its thickness, with the largest EDW variability along the Gulf Stream (GS) path. The EDW formation rate in GFDL CM2.1 is very weak because of weak heat loss from the ocean in the model. Unlike the observed dominant southward movement of the EDW, the EDW in GFDL CM2.1 and CCSM3 moves eastward after formation in the excessively wide GS in the models. However, the GFDL CDA does not capture the observed thermal response of the overlying atmosphere to the ocean. Observations show a robust anticorrelation between the upper-ocean heat content and air–sea heat flux, with upper-ocean heat content leading air–sea heat flux by a few months. This anticorrelation is well captured by GFDL CM2.1 and CCSM3 but not by GFDL CDA. Only GFDL CM2.1 captures the observed anticorrelation between the upper-ocean heat content and EDW volume. This suggests that, although data assimilation corrects the readily observed variables, it degrades the model thermodynamic response to forcing.

A Decomposition of Feedback Contributions to Polar Warming Amplification

Abstract Polar surface temperatures are expected to warm 2–3 times faster than the global-mean surface temperature: a phenomenon referred to as polar warming amplification. Therefore, understanding the individual process contributions to the polar warming is critical to understanding global climate sensitivity. The Coupled Feedback Response Analysis Method (CFRAM) is applied to decompose the annual- and zonal-mean vertical temperature response within a transient 1% yr−1 CO2 increase simulation of the NCAR Community Climate System Model, version 4 (CCSM4), into individual radiative and nonradiative climate feedback process contributions. The total transient annual-mean polar warming amplification (amplification factor) at the time of CO2 doubling is +2.12 (2.3) and +0.94 K (1.6) in the Northern and Southern Hemisphere, respectively. Surface albedo feedback is the largest contributor to the annual-mean polar warming amplification accounting for +1.82 and +1.04 K in the Northern and Southern Hemisphere, respectively. Net cloud feedback is found to be the second largest contributor to polar warming amplification (about +0.38 K in both hemispheres) and is driven by the enhanced downward longwave radiation to the surface resulting from increases in low polar water cloud. The external forcing and atmospheric dynamic transport also contribute positively to polar warming amplification: +0.29 and +0.32 K, respectively. Water vapor feedback contributes negatively to polar warming amplification because its induced surface warming is stronger in low latitudes. Ocean heat transport storage and surface turbulent flux feedbacks also contribute negatively to polar warming amplification. Ocean heat transport and storage terms play an important role in reducing the warming over the Southern Ocean and Northern Atlantic Ocean.

Atlantic Warm Pool Variability in the CMIP5 Simulations

AbstractThis study investigates Atlantic warm pool (AWP) variability in the historical run of 19 coupled general circulation models (CGCMs) submitted to phase 5 of the Coupled Model Intercomparison Project (CMIP5). As with the CGCMs in phase 3 (CMIP3), most models suffer from the cold SST bias in the AWP region and also show very weak AWP variability as represented by the AWP area index. However, for the seasonal cycle the AWP SST bias of model ensemble and model sensitivities are decreased compared with CMIP3, indicating that the CGCMs are improved. The origin of the cold SST bias in the AWP region remains unknown, but among the CGCMs in CMIP5 excess (insufficient) high-level cloud simulation decreases (enhances) the cold SST bias in the AWP region through the warming effect of the high-level cloud radiative forcing. Thus, the AWP SST bias in CMIP5 is more modulated by an erroneous radiation balance due to misrepresentation of high-level clouds rather than low-level clouds as in CMIP3. AWP variability is assessed as in the authors' previous study in the aspects of spectral analysis, interannual variability, multidecadal variability, and comparison of the remote connections with ENSO and the North Atlantic Oscillation (NAO) against observations. In observations the maximum influences of the NAO and ENSO on the AWP take place in boreal spring. For some CGCMs these influences erroneously last to late summer. The effect of this overestimated remote forcing can be seen in the variability statistics as shown in the rotated EOF patterns from the models. It is concluded that the NCAR Community Climate System Model, version 4 (CCSM4), the Goddard Institute for Space Studies (GISS) Model E, version 2, coupled with the Hybrid Coordinate Ocean Model (HYCOM) ocean model (GISS-E2H), and the GISS Model E, version 2, coupled with the Russell ocean model (GISS-E2R) are the best three models of CMIP5 in simulating AWP variability.

The Atlantic Meridional Heat Transport at 26.5°N and Its Relationship with the MOC in the RAPID Array and the GFDL and NCAR Coupled Models

Abstract The link at 26.5°N between the Atlantic meridional heat transport (MHT) and the Atlantic meridional overturning circulation (MOC) is investigated in two climate models, the GFDL Climate Model version 2.1 (CM2.1) and the NCAR Community Climate System Model version 4 (CCSM4), and compared with the recent observational estimates from the Rapid Climate Change–Meridional Overturning Circulation and Heatflux Array (RAPID–MOCHA) array. Despite a stronger-than-observed MOC magnitude, both models underestimate the mean MHT at 26.5°N because of an overly diffuse thermocline. Biases result from errors in both overturning and gyre components of the MHT. The observed linear relationship between MHT and MOC at 26.5°N is realistically simulated by the two models and is mainly due to the overturning component of the MHT. Fluctuations in overturning MHT are dominated by Ekman transport variability in CM2.1 and CCSM4, whereas baroclinic geostrophic transport variability plays a larger role in RAPID. CCSM4, which has a parameterization of Nordic Sea overflows and thus a more realistic North Atlantic Deep Water (NADW) penetration, shows smaller biases in the overturning heat transport than CM2.1 owing to deeper NADW at colder temperatures. The horizontal gyre heat transport and its sensitivity to the MOC are poorly represented in both models. The wind-driven gyre heat transport is northward in observations at 26.5°N, whereas it is weakly southward in both models, reducing the total MHT. This study emphasizes model biases that are responsible for the too-weak MHT, particularly at the western boundary. The use of direct MHT observations through RAPID allows for identification of the source of the too-weak MHT in the two models, a bias shared by a number of Coupled Model Intercomparison Project phase 5 (CMIP5) coupled models.

A Pseudoproxy Evaluation of Bayesian Hierarchical Modeling and Canonical Correlation Analysis for Climate Field Reconstructions over Europe*

Abstract A pseudoproxy comparison is presented for two statistical methods used to derive annual climate field reconstructions (CFRs) for Europe. The employed methods use the canonical correlation analysis (CCA) procedure presented by Smerdon et al. and the Bayesian hierarchical model (BHM) method adopted from Tingley and Huybers. Pseudoproxy experiments (PPEs) are constructed from modeled temperature data sampled from the 1250-yr paleo-run of the NCAR Community Climate System Model (CCSM) version 1.4 model by Ammann et al. Pseudoproxies approximate the distribution of the multiproxy network used by Mann et al. over the European region of interest. Gaussian white noise is added to the temperature data to mimic the combined signal and noise properties of real-world proxies. Results indicate that, while both methods perform well in areas with good proxy coverage, the BHM method outperforms the CCA method across the entire field and additionally returns objective error estimates.

Understanding the ENSO–CO2 Link Using Stabilized Climate Simulations

Abstract The influence of atmospheric CO2 concentration on the El Niño–Southern Oscillation (ENSO) is explored using 800-yr integrations of the NCAR Community Climate System Model, version 3.5 (CCSM3.5), with CO2 stabilized at the a.d. 1850, 1990, and 2050 levels. Model mean state changes with increased CO2 include preferential SST warming in the eastern equatorial Pacific, a weakening of the equatorial trade winds, increased vertical ocean stratification, and a reduction in the atmospheric Hadley and oceanic subtropical overturning circulations. The annual cycle of SST strengthens with CO2, likely related to unstable air–sea interactions triggered by an increased Northern Hemisphere land–sea temperature contrast. The mean trade wind structure changes asymmetrically about the equator, with increased convergence in the Northern Hemisphere and divergence in the Southern Hemisphere leading to corresponding deepening and shoaling of the thermocline. The proportion of eastern versus central Pacific–type El Niño events increases with CO2, but the significance of the changes is relatively low; ENSO amplitude also increases with CO2, although the change is insignificant at periods longer than 4 yr. The 2–4-yr ENSO response shows an enhancement in equatorial Kelvin wave variability, suggesting that stochastic triggering of El Niño events may be favored with higher CO2. However, the seasonal cycle–ENSO interaction is also modified by the asymmetric climatological changes, and forcing by the Southern Hemisphere becomes more important with higher CO2. Finally, higher-resolution CCSM4 control simulations show that ENSO weakens with CO2 given a sufficiently long integration time. The cause for the difference in ENSO climate sensitivity is not immediately obvious but may potentially be related to changes in westerly wind bursts or other sources of high-frequency wind stress variability.

Local and Remote Climate Impacts from Expansion of Woody Biomass for Bioenergy Feedstock in the Southeastern United States

Many efforts have been taken to find energy alternatives to reduce anthropogenic influences on climate. Recent studies have shown that using land for bioenergy plantations may be more cost effective and provide a greater potential for CO2 abatement than using land for carbon sequestration. Native southern U.S. pines (i.e., loblolly) have excellent potential as bioenergy feedstocks. However, the land-cover change due to expansion of biofuels may impact climate through biophysical feedbacks. Here, the authors access the local and remote consequences of greater forest management and biofuel feedstock expansion on climate and hydrology using a global climate model, the NCAR Community Climate System Model, version 4 (CCSM4). The authors examine a plausible U.S. Department of Energy (DOE) biofuel feedstock goal by afforesting 50 million acres of C4 grasslands in the southeastern United States with an optimized loblolly plant functional type. Changes in sensible and latent heat fluxes are related to increased surface roughness, reduced bare-ground evaporation, and changes in stomatal conductance. In the coupled simulations, these mechanisms lead to a 1°C cooling, higher atmospheric stability, and a more shallow planetary boundary layer over the southeastern United States during the summer; in winter, a cooling of up to 0.25°C between 40° and 60°N, a weakened Aleutian low, and a wetter Australia occurs. A weakened Aleutian low shifts the North Pacific storm track poleward in the future loblolly scenarios. These local and global impacts suggest that biophysical feedbacks need to be considered when evaluating the benefits of bioenergy feedstock production.