Biogeophysical impacts of land use on present‐day climate: near‐surface temperature change and radiative forcing

Schlosser, C. Adam; Dirmeyer, Paul A.

doi:10.1006/asle.2001.0037

Cited by 69 publications

(23 citation statements)

References 21 publications

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“…Temperate forests caused cooling in warm seasons mainly due to stronger ET than cropland, while in winter, when the ET cooling was weak and albedo warming became dominated, forest showed local warming. As both observation and modeling studies confirmed that forests had lower albedo than short vegetation [Betts, 2001;, we inferred that the differences between our seasonal pattern and previous modeling results may partly come from the discrepancy between observed and simulated ETs. Our result is generally supported by field sites measurements [Juang et al, 2007] and satellite observations [Peng et al, 2014;Zhao and Jackson, 2014], though there are some minor discrepancy over different case study areas and periods.…”

Section: Effects Of Forest Conversion On δLstsupporting

confidence: 50%

“…However, most climate model simulations conclude that the overall climate effect of temperate forest removal is cooling [Bonan, 1997[Bonan, , 1999Bounoua et al, 2002;Betts, 2001;Davin and de Noblet-Ducoudre, 2010]. Such inconsistency with the satellite-based study may be due to the following two reasons: (1) Some uncertainties in scenario-based models may exaggerate the actual temperature effect [Oleson et al, 2004], such as key physical processes (e.g., evapotranspiration process), parameterization of land surface processes [Pielke et al, 2011], models representing vegetation dynamics [Mahmood et al, 2010], and oceanic dynamics [Feddema et al, 2005].…”

Section: Effects Of Forest Conversion On δLstmentioning

confidence: 99%

“…The sign and magnitude of biophysical effect in temperate zones are still under debate around the following viewpoints. First, most of scenario-based climate models showed that the removal of temperate forest led to lower surface temperature [Bonan, 1997[Bonan, , 1999Bounoua et al, 2002;Betts, 2001;Davin and de Noblet-Ducoudre, 2010] and the magnitude depended on the land cover classification and land biophysics parameterization used in the models [Oleson et al, 2004]. Second, most field and satellite observations [Juang et al, 2007;Montenegro et al, 2009;Wickham et al, 2013] and a few climate models [Jackson et al, 2005;Ma et al, 2013] showed the opposite results, i.e., temperate forest cooled surface or air temperature relative to cropland and pastures.…”

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confidence: 99%

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Multiple satellite‐based analysis reveals complex climate effects of temperate forests and related energy budget

Jia

Zhang

2017

JGR Atmospheres

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Forest conversion‐driven biophysical processes have been examined in various case studies that largely depend on sensitivity analysis of climate modeling. However, much remains unknown in the real world due to the complicated process and uncertainty in magnitude, especially in the temperate bioclimate regions. This study applied satellite‐based observation to investigate the biophysical climate response to potential forest conversion in China, especially on the spatial and temporal patterns and underlying mechanisms. We evaluated the differences of land surface temperature (ΔLST) between adjacent forest and cropland, in terms of the latitudinal and seasonal patterns. Compared to cropland, the temperate forest to the south of 40°N showed the cooling effect of −0.61 ± 0.02°C (95% confidence interval, and hereafter), and it presented the warming effect of 0.48 ± 0.06°C to the north of 48°N (the transition zone was between 40°N and 48°N). Seasonal analysis further demonstrated that the cooling effects of temperate forest in China in spring (March, April, May), summer (June, July, August), and autumn (September, October, November) were −0.53 ± 0.02°C, −0.55 ± 0.02°C, and −0.30 ± 0.02°C, respectively, while the forest caused the warming effect of 0.10 ± 0.04°C in winter (December, January, February). However, the biophysical climate response to forest conversion in temperate regions was complex and showed highly spatial and temporal heterogeneity. We further assessed the role of two major biophysical processes, i.e., albedo and evapotranspiration (ET), in shaping land surface temperature from surface energy budget perspective. Results showed that the latitudinal, seasonal, and spatiotemporal patterns of ΔLST was determined by the net effect of ET‐induced latent heat changes and albedo‐induced solar radiation absorption changes.

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Section: Effects Of Forest Conversion On δLstsupporting

confidence: 50%

Section: Effects Of Forest Conversion On δLstmentioning

confidence: 99%

mentioning

confidence: 99%

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Multiple satellite‐based analysis reveals complex climate effects of temperate forests and related energy budget

Jia

Zhang

2017

JGR Atmospheres

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“…Previous studies [e.g., Schlosser and Dirmeyer , 2001] have shown that multidecade simulations such as AMIP and C20C can produce exaggerated and potentially erroneous responses over continental regions to SST variations. These erroneous responses are likely due to climate drift over land [e.g., Dirmeyer , 2001].…”

Section: Discussion and Closing Remarksmentioning

confidence: 99%

Predictable skill and its association to sea‐surface temperature variations in an ensemble climate simulation

Schlosser

Kirtman

2005

J. Geophys. Res.

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[1] Simulated near-surface air temperature (T a ) and precipitation in an ensemble climate simulation is assessed. The ensemble climate-simulation was constructed with the Center for Ocean-Land-Atmosphere (COLA) atmospheric general circulation model (AGCM) in conjunction with the Atmospheric Model Intercomparison Project Phase II (AMIP II). To diagnose the ensemble simulation, a measure of ''predictable skill'' is formalized. This diagnostic is based upon the statistical significance of spatial correlation over any given region (for this analysis, North America) between the ensemble mean and observed anomalies and the inter-member scatter of the spatial anomaly correlation. Using this measure as a function of time, periods of predictable skill within the COLA AMIP II ensemble simulation are identified, and through a point-wise, multiple correlation technique, spatial patterns of contemporaneous sea-surface temperature (SST) variability are also constructed. This diagnosis can essentially define which skillfully simulated climate signals over North America (given by predictable skill) can be attributed to specific SST anomalies. Spatially coherent patterns of SST variability are found to be associated with predictable skill. These patterns are, not surprisingly, primarily related to the El Niño Southern Oscillation (ENSO). One of the more prevalent ENSO associations to predictable skill (for the COLA AGCM) is found in sub-tropical regions of the western Pacific. Moreover, a skillful episode of simulated North American precipitation is seen to be associated with SST variability over the east Indian Ocean. In addition, a strong contemporaneous association of T a predictable skill with tropical Atlantic SST variability is found. Complementary simulations are performed with the COLA AGCM to further assess the degree of attribution of predictable skill to these regions of SST variability. The results of the experimental simulations suggest that, for the AMIP II simulation period, the COLA AGCM can be skillfully attributed to SST variability primarily associated with ENSO, warm tropical Atlantic anomalies, and an abrupt, cooling event over the east Indian Ocean.Citation: Schlosser, C. A., and B. P. Kirtman (2005), Predictable skill and its association to sea-surface temperature variations in an ensemble climate simulation,

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“…The physical basis for atmospheric predictability at seasonal scale is related first to the impact on the atmosphere of the slower variations due to external forcing that are generally associated to the SST anomalies (e.g. Brankovic et al, 1994;Zwiers, 1996;Rowell, 1998;Frederiksen et al, 2001;Schlosser and Dirmeyer, 2001;Marengo et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of regional climatic simulation over Southeastern South America to SST specification during austral summer

Cuadra

Rocha

2006

Intl Journal of Climatology

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Abstract:The impact of the sea-surface temperature (SST) specification over the South Atlantic Ocean on the interannual variability of the simulated precipitation and air temperature over Southeastern South America (SESA) was studied during ten austral summers, from 1989 to 1998, using the Regional Climate Model version 3 (RegCM3), driven by the National Centers for Environmental Prediction (NCEP) reanalysis. The analyses were performed by comparing simulations forced with: (1) monthly observed SST (OSST); (2) SST composed by the persistence of November SST anomalies superimposed on the climatological observed cycle (persisted SST-PSST). The results show that, over the continental SESA, the PSST impact is low on the simulated seasonal precipitation and air temperature climatologies. Furthermore, the interannual seasonal anomalies in this area do not change sign as a function of the prescribed SST. This is very important since it shows that, on a seasonal timescale, the use of PSST over the subtropical South Atlantic does not introduce additional error source in the regional model simulations over the continental SESA. Over the Atlantic Ocean, mainly in the region where the South Atlantic Convergence Zone (SACZ) acts, the simulations presented a direct response as a function of the SST, i.e. if the PSST is warmer (colder) than the OSST, the seasonal precipitation increases (decreases).Ensemble simulations of a particular summer showed that the deviation introduced by PSST is of the same intensity of the model internal variability and the use of five members ensemble-mean eliminates the differences between OSST and PSST experiments over continental SESA. However, this result cannot be applied over the subtropical South Atlantic Ocean, where the differences were not eliminated by calculating the ensemble-mean, showing a signal generated by the prescribed SST.

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Biogeophysical impacts of land use on present‐day climate: near‐surface temperature change and radiative forcing

Cited by 69 publications

References 21 publications

Multiple satellite‐based analysis reveals complex climate effects of temperate forests and related energy budget

Multiple satellite‐based analysis reveals complex climate effects of temperate forests and related energy budget

Predictable skill and its association to sea‐surface temperature variations in an ensemble climate simulation

Sensitivity of regional climatic simulation over Southeastern South America to SST specification during austral summer

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