scholarly journals Radiative forcing bias of simulated surface albedo modifications linked to forest cover changes at northern latitudes

2015 ◽  
Vol 12 (7) ◽  
pp. 2195-2205 ◽  
Author(s):  
R. M. Bright ◽  
G. Myhre ◽  
R. Astrup ◽  
C. Antón-Fernández ◽  
A. H. Strømman
Keyword(s):  
Radiative Forcing ◽  
Surface Albedo ◽  
Forest Cover ◽  
Climate Models ◽  
Open Area ◽  
Data Set ◽  
Radiative Forcings ◽  
Open Forest ◽  
Northern Latitudes ◽  
Analytical Time

Abstract. In the presence of snow, the bias in the prediction of surface albedo by many climate models remains difficult to correct due to the difficulties of separating the albedo parameterizations from those describing snow and vegetation cover and structure. This can be overcome by extracting the albedo parameterizations in isolation, by executing them with observed meteorology and information on vegetation structure, and by comparing the resulting predictions to observations. Here, we employ an empirical data set of forest structure and daily meteorology for three snow cover seasons and for three case regions in boreal Norway to compute and evaluate predicted albedo to those based on daily MODIS retrievals. Forest and adjacent open area albedos are subsequently used to estimate bias in top-of-the-atmosphere (TOA) radiative forcings (RF) from albedo changes (Δα, Open–Forest) connected to land use and land cover changes (LULCC). As expected, given the diversity of approaches by which snow masking by tall-statured vegetation is parameterized, the magnitude and sign of the albedo biases varied considerably for forests. Large biases at the open sites were also detected, which was unexpected given that these sites were snow-covered throughout most of the analytical time period, therefore eliminating potential biases linked to snow-masking parameterizations. Biases at the open sites were mostly positive, exacerbating the strength of vegetation masking effects and hence the simulated LULCC Δα RF. Despite the large biases in both forest and open area albedos by some schemes in some months and years, the mean Δα RF bias over the 3-year period (November–May) was considerably small across models (−2.1 ± 1.04 Wm−2; 21 ± 11%); four of six models had normalized mean absolute errors less than 20%. Identifying systematic sources of the albedo prediction biases proved challenging, although for some schemes clear sources were identified.

scholarly journals Radiative forcing bias of surface albedo modifications linked to simulated forest cover changes at northern latitudes

2014 ◽  
Vol 11 (12) ◽  
pp. 17339-17360
Author(s):  
R. M. Bright ◽  
G. Myhre ◽  
R. Astrup ◽  
C. Antón-Fernández ◽  
A. H. Strømman
Keyword(s):  
Land Surface ◽  
Radiative Forcing ◽  
Surface Albedo ◽  
Forest Cover ◽  
Climate Models ◽  
Land Use Land Cover ◽  
Radiative Forcings ◽  
Northern Latitudes ◽  
Forest Cover Changes ◽  
Model Improvement

Abstract. Simulated land use/land cover change (LULCC) radiative forcings (RF) from changes in surface albedo (Δα) predicted by land surface schemes of six leading climate models were compared to those based on daily MODIS retrievals for three regions in Norway and for three winter–spring seasons. As expected, the magnitude and sign of the albedo biases varied considerably for forests; unexpectedly, however, biases of equal magnitude were evident in predictions at open area sites. The latter were mostly positive and exacerbated the strength of vegetation masking effects and hence the simulated LULCC Δα RF. RF bias was considerably small across models (-0.08 ± 0.04 W m-2; 21 ± 11%); 4 of 6 models had normalized mean absolute errors less than 20% (3-year regional mean). Identifying systematic sources of the albedo prediction biases proved challenging, although for some schemes clear sources were identified. Our study should provide some reassurance that model improvement efforts of recent years are leading to enhanced LULCC climate predictions.

scholarly journals Comparison of three vertically resolved ozone data bases: climatology, trends and radiative forcings

2012 ◽  
Vol 12 (10) ◽  
pp. 26561-26605 ◽  
Author(s):  
B. Hassler ◽  
P. J. Young ◽  
R. W. Portmann ◽  
G. E. Bodeker ◽  
J. S. Daniel ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Climate Models ◽  
Linear Trend ◽  
Piecewise Linear ◽  
Stratospheric Ozone ◽  
Data Sets ◽  
Heating Rates ◽  
Data Set ◽  
Radiative Forcings ◽  
Ozone Trends

Abstract. Climate models that do not simulate changes in stratospheric ozone concentrations require ozone input fields to accurately calculate UV fluxes and stratospheric heating rates. In this study, three different global ozone time series that are available for this purpose are compared: the data set of Randel and Wu (2007) (RW07), Cionni et al. (2011) (SPARC), and Bodeker et al. (2012) (BDBP). The latter is a very recent data set, based on the comprehensive ozone measurement database described by Hassler et al. (2008). All three data sets represent multiple-linear regression fits to vertically resolved ozone observations, resulting in a patially and temporally continuous stratospheric ozone field covering at least the period from 1979 to 2005. The main difference between the data sets result from using different observations and including different basis functions for the regression model fits. These three regression-based data sets are compared against observations from ozonesondes and satellites to compare how the data sets represent concentrations, trends, and interannual variability. In the Southern Hemisphere polar region, RW07 and SPARC underestimate the ozone depletion in spring as seen in ozonesonde measurements. A piecewise linear trend regression is performed to estimate the 1979–1996 ozone decrease globally, covering a period of extreme depletion in most regions. BDBP seems to overestimate Arctic and tropical ozone loss over this period somewhat relative to the available measurements, whereas these appear to be underestimated in RW07 and SPARC. In most regions, the three data sets yield ozone values that are within the range of the different observations that serve as input to the regressions. However, the differences among the three suggest that there are large uncertainties in ozone trends. These result in differences of almost a factor of four in radiative forcing, which is important for the resulting climate changes.

scholarly journals Total aerosol effect: radiative forcing or radiative flux perturbation?

2009 ◽  
Vol 9 (6) ◽  
pp. 25633-25661 ◽  
Author(s):  
U. Lohmann ◽  
L. Rotstayn ◽  
T. Storelvmo ◽  
A. Jones ◽  
S. Menon ◽  
...  
Keyword(s):  
Global Warming ◽  
Radiative Forcing ◽  
Climate Models ◽  
Radiative Flux ◽  
Radiative Forcings ◽  
Microphysical Properties ◽  
Aerosol Effect ◽  
Rain Formation ◽  
Aerosol Radiative ◽  
Flux Perturbation

Abstract. Uncertainties in aerosol radiative forcings, especially those associated with clouds, contribute to a large extent to uncertainties in the total anthropogenic forcing. The interaction of aerosols with clouds and radiation introduces feedbacks which can affect the rate of rain formation. In former assessments of aerosol radiative forcings, these effects have not been quantified. Also, with global aerosol-climate models simulating interactively aerosols and cloud microphysical properties, a quantification of the aerosol forcings in the traditional way is difficult to properly define. Here we argue that fast feedbacks should be included because they act quickly compared with the time scale of global warming. We show that for different forcing agents (aerosols and greenhouse gases) the radiative forcings as traditionally defined agree rather well with estimates from a method, here referred to as radiative flux perturbations (RFP), that takes these fast feedbacks and interactions into account. Based on our results, we recommend RFP as a valid option to compare different forcing agents, and to compare the effects of particular forcing agents in different models.

scholarly journals Including the efficacy of land ice changes in deriving climate sensitivity from paleodata

2019 ◽  
Vol 10 (2) ◽  
pp. 333-345 ◽  
Author(s):  
Lennert B. Stap ◽  
Peter Köhler ◽  
Gerrit Lohmann
Keyword(s):  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Climate Models ◽  
Co2 Concentration ◽  
Global Temperature ◽  
Maximum Temperature ◽  
Radiative Forcings ◽  
The Impact ◽  
The Last Glacial

Abstract. The equilibrium climate sensitivity (ECS) of climate models is calculated as the equilibrium global mean surface air warming resulting from a simulated doubling of the atmospheric CO2 concentration. In these simulations, long-term processes in the climate system, such as land ice changes, are not incorporated. Hence, climate sensitivity derived from paleodata has to be compensated for these processes, when comparing it to the ECS of climate models. Several recent studies found that the impact these long-term processes have on global temperature cannot be quantified directly through the global radiative forcing they induce. This renders the prevailing approach of deconvoluting paleotemperatures through a partitioning based on radiative forcings inaccurate. Here, we therefore implement an efficacy factor ε[LI] that relates the impact of land ice changes on global temperature to that of CO2 changes in our calculation of climate sensitivity from paleodata. We apply our refined approach to a proxy-inferred paleoclimate dataset, using ε[LI]=0.45-0.20+0.34 based on a multi-model assemblage of simulated relative influences of land ice changes on the Last Glacial Maximum temperature anomaly. The implemented ε[LI] is smaller than unity, meaning that per unit of radiative, forcing the impact on global temperature is less strong for land ice changes than for CO2 changes. Consequently, our obtained ECS estimate of 5.8±1.3 K, where the uncertainty reflects the implemented range in ε[LI], is ∼50 % higher than when differences in efficacy are not considered.

scholarly journals Complexity of Snow Schemes in a Climate Model and Its Impact on Surface Energy and Hydrology

2012 ◽  
Vol 13 (2) ◽  
pp. 521-538 ◽  
Author(s):  
Emanuel Dutra ◽  
Pedro Viterbo ◽  
Pedro M. A. Miranda ◽  
Gianpaolo Balsamo
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Climate Models ◽  
Climate Model ◽  
Single Layer ◽  
Atmospheric Temperature ◽  
Soil Freezing ◽  
Near Surface ◽  
Northern Latitudes ◽  
Tightly Coupled

Abstract Three different complexity snow schemes implemented in the ECMWF land surface scheme Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) are evaluated within the EC-EARTH climate model. The snow schemes are (i) the original HTESSEL single-bulk-layer snow scheme, (ii) a new snow scheme in operations at ECMWF since September 2009, and (iii) a multilayer version of the previous. In offline site simulations, the multilayer scheme outperforms the single-layer schemes in deep snowpack conditions through its ability to simulate sporadic melting events thanks to the lower thermal inertial of the uppermost layer. Coupled atmosphere–land/snow simulations performed by the EC-EARTH climate model are validated against remote sensed snow cover and surface albedo. The original snow scheme has a systematic early melting linked to an underestimation of surface albedo during spring that was partially reduced with the new snow schemes. A key process to improve the realism of the near-surface atmospheric temperature and at the same time the soil freezing is the thermal insulation of the snowpack (tightly coupled with the accuracy of snow mass and density simulations). The multilayer snow scheme outperforms the single-layer schemes in open deep snowpack (such as prairies or tundra in northern latitudes) and is instead comparable in shallow snowpack conditions. However, the representation of orography in current climate models implies limitations for accurately simulating the snowpack, particularly over complex terrain regions such as the Rockies and the Himalayas.

Albedo observations of the Earth’s surface for climate research

1983 ◽  
Vol 309 (1508) ◽  
pp. 285-294 ◽  
Keyword(s):  
General Circulation ◽  
Surface Albedo ◽  
Climate Models ◽  
General Circulation Models ◽  
Data Set ◽  
Primary Input ◽  
Climate Research ◽  
Earth’S Climate ◽  
Atmospheric Distortion ◽  
Spectrally Resolved

The primary input of energy to the Earth’s climate system occurs at the surface and can be highly sensitive to the surface albedo. Albedo changes have been proposed as one cause of climatic variation, but results from climate models are not yet consistent. It is very difficult to establish an agreed global data set with which to initiate comparative climatic simulations. Albedo observations must be spectrally resolved because reflexion of solar radiation is a strong function of wavelength and incident and reflected beams are modified by the atmosphere. Parametrization of system albedos in energybalance models draws on satellite data. The use of satellite observations is less easy in general circulation climate models. The removal of atmospheric distortion is particularly difficult. The establishment of a surface albedo data set generally follows one of two approaches: geographical categorization or remote monitoring. Surface albedo specification in current general circulation models is diverse. This paper reviews the ways in which remotely derived albedo measurements are used now and may, in the future, be improved for climate research.

scholarly journals Comparison of Anthropogenic Aerosol Climate Effects among Three Climate Models with Reduced Complexity

Atmosphere ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 456 ◽  
Author(s):  
Xiangjun Shi ◽  
Wentao Zhang ◽  
Jiaojiao Liu
Keyword(s):  
Radiative Forcing ◽  
Climate Models ◽  
Reliable Estimate ◽  
Anthropogenic Aerosol ◽  
Climate Effects ◽  
Cloud Forcing ◽  
Radiative Forcings ◽  
Ensemble Simulations ◽  
Aerosol Forcing ◽  
The Difference

The same prescribed anthropogenic aerosol forcing was implemented into three climate models. The atmosphere components of these participating climate models were the GAMIL, ECHAM, and CAM models. Ensemble simulations were carried out to obtain a reliable estimate of anthropogenic aerosol effective radiative forcing (ERF). The ensemble mean ERFs from these three participating models with this aerosol forcing were −0.27, −0.63, and −0.54 W∙m−2. The model diversity in ERF is clearly reduced as compared with those based on the models’ own default approaches (−1.98, −0.21, and −2.22 W∙m−2). This is consistent with the design of this aerosol forcing. The modeled ERF can be decomposed into two basic components, i.e., the instantaneous radiative forcing (RF) from aerosol–radiation interactions (RFari) and the aerosol-induced changes in cloud forcing (△Fcloud*). For the three participating models, the model diversity in RFari (−0.21, −0.33, and −0.29 W∙m−2) could be constrained by reducing the differences in natural aerosol radiative forcings. However, it was difficult to figure out the reason for the model diversity in △Fcloud* (−0.05, −0.28, and −0.24 W∙m−2), which was the dominant source of the model diversity in ERF. The variability of modeled ERF was also studied. Ensemble simulations showed that the modeled RFs were very stable. The rapid adjustments (ERF − RF) had an important role to play in the quantification of the perturbation of ERF. Fortunately, the contribution from the rapid adjustments to the mean ERF was very small. This study also showed that we should pay attention to the difference between the aerosol climate effects we want and the aerosol climate effects we calculate.

scholarly journals Comparison of three vertically resolved ozone data sets: climatology, trends and radiative forcings

2013 ◽  
Vol 13 (11) ◽  
pp. 5533-5550 ◽  
Author(s):  
B. Hassler ◽  
P. J. Young ◽  
R. W. Portmann ◽  
G. E. Bodeker ◽  
J. S. Daniel ◽  
...  
Keyword(s):  
Ozone Depletion ◽  
Radiative Forcing ◽  
Climate Models ◽  
Linear Trend ◽  
Piecewise Linear ◽  
Stratospheric Ozone ◽  
Data Sets ◽  
Heating Rates ◽  
Data Set ◽  
Ozone Concentrations

Abstract. Climate models that do not simulate changes in stratospheric ozone concentrations require the prescription of ozone fields to accurately calculate UV fluxes and stratospheric heating rates. In this study, three different global ozone time series that are available for this purpose are compared: the data set of Randel and Wu (2007) (RW07), Cionni et al. (2011) (SPARC), and Bodeker et al. (2013) (BDBP). All three data sets represent multiple-linear regression fits to vertically resolved ozone observations, resulting in a spatially and temporally continuous stratospheric ozone field covering at least the period from 1979 to 2005. The main differences among the data sets result from regression models, which use different observations and include different basis functions. The data sets are compared against ozonesonde and satellite observations to assess how the data sets represent concentrations, trends and interannual variability. In the Southern Hemisphere polar region, RW07 and SPARC underestimate the ozone depletion in spring ozonesonde measurements. A piecewise linear trend regression is performed to estimate the 1979–1996 ozone decrease globally, covering a period of extreme depletion in most regions. BDBP overestimates Arctic and tropical ozone depletion over this period relative to the available measurements, whereas the depletion is underestimated in RW07 and SPARC. While the three data sets yield ozone concentrations that are within a range of different observations, there is a large spread in their respective ozone trends. One consequence of this is differences of almost a factor of four in the calculated stratospheric ozone radiative forcing between the data sets (RW07: −0.038 Wm−2, SPARC: −0.033 Wm−2, BDBP: −0.119 Wm−2), important in assessing the contribution of stratospheric ozone depletion to the total anthropogenic radiative forcing.

scholarly journals Total aerosol effect: radiative forcing or radiative flux perturbation?

2010 ◽  
Vol 10 (7) ◽  
pp. 3235-3246 ◽  
Author(s):  
U. Lohmann ◽  
L. Rotstayn ◽  
T. Storelvmo ◽  
A. Jones ◽  
S. Menon ◽  
...  
Keyword(s):  
Global Warming ◽  
Radiative Forcing ◽  
Climate Models ◽  
Radiative Flux ◽  
Radiative Forcings ◽  
Microphysical Properties ◽  
Aerosol Effect ◽  
Precipitation Formation ◽  
Aerosol Radiative ◽  
Flux Perturbation

Abstract. Uncertainties in aerosol radiative forcings, especially those associated with clouds, contribute to a large extent to uncertainties in the total anthropogenic forcing. The interaction of aerosols with clouds and radiation introduces feedbacks which can affect the rate of precipitation formation. In former assessments of aerosol radiative forcings, these effects have not been quantified. Also, with global aerosol-climate models simulating interactively aerosols and cloud microphysical properties, a quantification of the aerosol forcings in the traditional way is difficult to define properly. Here we argue that fast feedbacks should be included because they act quickly compared with the time scale of global warming. We show that for different forcing agents (aerosols and greenhouse gases) the radiative forcings as traditionally defined agree rather well with estimates from a method, here referred to as radiative flux perturbations (RFP), that takes these fast feedbacks and interactions into account. Based on our results, we recommend RFP as a valid option to compare different forcing agents, and to compare the effects of particular forcing agents in different models.

scholarly journals Including the efficacy of land ice changes in deriving climate sensitivity from paleodata

2018 ◽  
Author(s):  
Lennert B. Stap ◽  
Peter Köhler ◽  
Gerrit Lohmann
Keyword(s):  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Temperature Anomaly ◽  
Global Temperature ◽  
Radiative Forcings ◽  
Intercomparison Project ◽  
The Impact

Abstract. The influence of long-term processes in the climate system, such as land ice changes, has to be compensated for when comparing climate sensitivity derived from paleodata with equilibrium climate sensitivity (ECS) calculated by climate models, which is only generated by a CO2 change. Several recent studies found that the impact these long-term processes have on global temperature cannot be quantified directly through the global radiative forcing they induce. This renders the approach of deconvoluting paleotemperatures through a partitioning based on radiative forcings inaccurate. Here, we therefore implement an efficacy factor ε[LI], that relates the impact of land ice changes on global temperature to that of CO2 changes, in our calculation of climate sensitivity from paleodata. We apply our new approach to a proxy-inferred paleoclimate dataset, and find an equivalent ECS of 5.6 ± 1.3 K per CO2 doubling. The substantial uncertainty herein is generated by the range in ε[LI] we use, which is based on a multi-model assemblage of simulated relative influences of land ice changes on the Last Glacial Maximum (LGM) temperature anomaly (46 ± 14 %). The low end of our ECS estimate, which concurs with estimates from other approaches, tallies with a large influence for land ice changes. To separately assess this influence, we analyse output of the PMIP3 climate model intercomparison project. From this data, we infer a functional intermodel relation between global and high-latitude temperature changes at the LGM with respect to the pre-industrial climate, and the temperature anomaly caused by a CO2 change. Applying this relation to our dataset, we find a considerable 64 % influence for land ice changes on the LGM temperature anomaly. This is even higher than the range used before, and leads to an equivalent ECS of 3.8 K per CO2 doubling. Together, our results suggest that land ice changes play a key role in the variability of Late Pleistocene temperatures.

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