Global analysis of radiative forcing from fire-induced shortwave albedo change

Abstract. Land surface albedo, a key parameter to derive Earth's surface energy balance, is used in the parameterization of numerical weather prediction, climate monitoring and climate change impact assessments. Changes in albedo due to fire have not been fully investigated on a continental and global scale. The main goal of this study, therefore, is to quantify the changes in instantaneous shortwave albedo produced by biomass burning activities and their associated radiative forcing. The study relies on the MODerate-resolution Imaging Spectroradiometer (MODIS) MCD64A1 burned-area product to create an annual composite of areas affected by fire and the MCD43C2 bidirectional reflectance distribution function (BRDF) albedo snow-free product to compute a bihemispherical reflectance time series. The approximate day of burning is used to calculate the instantaneous change in shortwave albedo. Using the corresponding National Centers for Environmental Prediction (NCEP) monthly mean downward solar radiation flux at the surface, the global radiative forcing associated with fire was computed. The analysis reveals a mean decrease in shortwave albedo of −0.014 (1σ = 0.017), causing a mean positive radiative forcing of 3.99 Wm−2 (1σ = 4.89) over the 2002–20012 time period in areas affected by fire. The greatest drop in mean shortwave albedo change occurs in 2002, which corresponds to the highest total area burned (378 Mha) observed in the same year and produces the highest mean radiative forcing (4.5 Wm−2). Africa is the main contributor in terms of burned area, but forests globally give the highest radiative forcing per unit area and thus give detectable changes in shortwave albedo. The global mean radiative forcing for the whole period studied (~0.0275 Wm−2) shows that the contribution of fires to the Earth system is not insignificant.

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Global analysis of radiative forcing from fire-induced shortwave albedo change

Biogeosciences Discussions ◽

10.5194/bgd-11-7775-2014 ◽

2014 ◽

Vol 11 (5) ◽

pp. 7775-7796 ◽

Cited By ~ 3

Author(s):

G. López-Saldaña ◽

I. Bistinas ◽

J. M. C. Pereira

Keyword(s):

Land Surface ◽

Radiative Forcing ◽

Global Analysis ◽

Weather Prediction ◽

Global Scale ◽

Burned Area ◽

Albedo Change ◽

Moderate Resolution Imaging Spectroradiometer ◽

Environmental Prediction ◽

Area Product

Abstract. Land surface albedo, a key parameter to derive Earth's surface energy balance, is used in the parameterization of numerical weather prediction, climate monitoring and climate change impact assessments. Changes in albedo due to fire have not been fully investigated at continental and global scale. The main goal of this study therefore, is to quantify the changes in albedo produced by biomass burning activities and their associated shortwave radiative forcing. The study relies on the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64A1 burned area product to create an annual composite of areas affected by fire and the MCD43C2 BRDF-Albedo snow-free product to compute a bihemispherical reflectance time series. The approximate day of burn is used to calculate the instantaneous change in shortwave Albedo. Using the corresponding National Centers for Environmental Prediction (NCEP) monthly mean downward solar radiation flux at the surface, the global radiative forcing associated to fire was computed. The analysis reveals a mean decrease in shortwave albedo of −0.023 (1σ = 0.018) causing a mean positive radiative forcing of 6.31 W m–2 (1σ = 5.04) over the 2002–2012 time period in areas affected by fire. The greatest drop in mean shortwave albedo change occurs in 2002, which corresponds to the highest total area burnt (3.66 Mha) observed in the same year and produces the highest mean radiative forcing (6.75 W m–2). Africa is the main contributor in terms of burned area but forests globally are giving the highest radiative forcing per unit area, thus give detectable changes in shortwave albedo. The global mean radiative forcing for the whole studied period ~ 0.04 W m–2 shows that the contribution of fires into the Earth system is not insignificant.

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Surface Emissivity Impact on Temperature and Moisture Soundings from Hyperspectral Infrared Radiance Measurements

Journal of Applied Meteorology and Climatology ◽

10.1175/2010jamc2587.1 ◽

2011 ◽

Vol 50 (6) ◽

pp. 1225-1235 ◽

Cited By ~ 15

Author(s):

Zhigang Yao ◽

Jun Li ◽

Jinlong Li ◽

Hong Zhang

Keyword(s):

Land Surface ◽

Prediction Models ◽

Weather Prediction ◽

Atmospheric Temperature ◽

Retrieval Algorithm ◽

Surface Emissivity ◽

Angle Dependence ◽

Infrared Radiance ◽

Moderate Resolution Imaging Spectroradiometer ◽

The Impact

AbstractAn accurate land surface emissivity (LSE) is critical for the retrieval of atmospheric temperature and moisture profiles along with land surface temperature from hyperspectral infrared (IR) sounder radiances; it is also critical to assimilating IR radiances in numerical weather prediction models over land. To investigate the impact of different LSE datasets on Atmospheric Infrared Sounder (AIRS) sounding retrievals, experiments are conducted by using a one-dimensional variational (1DVAR) retrieval algorithm. Sounding retrievals using constant LSE, the LSE dataset from the Infrared Atmospheric Sounding Interferometer (IASI), and the baseline fit dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS) are performed. AIRS observations over northern Africa on 1–7 January and 1–7 July 2007 are used in the experiments. From the limited regional comparisons presented here, it is revealed that the LSE from the IASI obtained the best agreement between the retrieval results and the ECMWF reanalysis, whereas the constant LSE gets the worst results when the emissivities are fixed in the retrieval process. The results also confirm that the simultaneous retrieval of atmospheric profile and surface parameters could reduce the dependence of soundings on the LSE choice and finally improve sounding accuracy when the emissivities are adjusted in the iterative retrieval. In addition, emissivity angle dependence is investigated with AIRS radiance measurements. The retrieved emissivity spectra from AIRS over the ocean reveal weak angle dependence, which is consistent with that from an ocean emissivity model. This result demonstrates the reliability of the 1DVAR simultaneous algorithm for emissivity retrieval from hyperspectral IR radiance measurements.

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The Ocean’s Role in Continental Climate Variability and Change

Journal of Climate ◽

10.1175/2009jcli2778.1 ◽

2009 ◽

Vol 22 (18) ◽

pp. 4939-4952 ◽

Cited By ~ 54

Author(s):

Dietmar Dommenget

Keyword(s):

Global Warming ◽

Climate Variability ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Radiative Forcing ◽

Global Scale ◽

Future Climate Change ◽

Climate Change Scenarios ◽

Continental Climate

Abstract A characteristic feature of global warming is the land–sea contrast, with stronger warming over land than over oceans. Recent studies find that this land–sea contrast also exists in equilibrium global change scenarios, and it is caused by differences in the availability of surface moisture over land and oceans. In this study it is illustrated that this land–sea contrast exists also on interannual time scales and that the ocean–land interaction is strongly asymmetric. The land surface temperature is more sensitive to the oceans than the oceans are to the land surface temperature, which is related to the processes causing the land–sea contrast in global warming scenarios. It suggests that the ocean’s natural variability and change is leading to variability and change with enhanced magnitudes over the continents, causing much of the longer-time-scale (decadal) global-scale continental climate variability. Model simulations illustrate that continental warming due to anthropogenic forcing (e.g., the warming at the end of the last century or future climate change scenarios) is mostly (80%–90%) indirectly forced by the contemporaneous ocean warming, not directly by local radiative forcing.

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A global analysis of satellite derived and DGVM surface soil moisture products

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-8-4281-2011 ◽

2011 ◽

Vol 8 (2) ◽

pp. 4281-4312

Author(s):

K. T. Rebel ◽

R. A. M. de Jeu ◽

P. Ciais ◽

N. Viovy ◽

S. L. Piao ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Root Zone ◽

Global Analysis ◽

Single Layer ◽

Global Scale ◽

Rain Event ◽

Deep Soil ◽

Surface Climate ◽

Soil Moisture Availability

Abstract. Soil moisture availability is important in regulating photosynthesis and controlling land surface-climate feedbacks at both the local and global scale. Recently, global remote-sensing datasets for soil moisture have become available. In this paper we assess the possibility of using remotely sensed soil moisture (AMSR-E) to evaluate the results of the process-based vegetation model ORCHIDEE during the period 2003–2004. We find that the soil moisture products of AMSR-E and ORCHIDEE correlate well, in particular when considering the root zone soil moisture of ORCHIDEE. However, the root zone soil moisture in ORCHIDEE consistently overestimated the temporal autocorrelation relative to AMSR-E and in situ measurements. This may be due to the different vertical depth of the two products, to the uncertainty in precipitation forcing in ORCHIDEE, and to the fact that the structure of ORCHIDEE consisting of a single-layer deep soil, does not allow simulation of the proper cascade of time scales that characterize soil drying after each rain event. We conclude that assimilating soil moisture in ORCHIDEE using AMSR-E with the current hydrological model may significantly improve the soil moisture dynamics in ORCHIDEE.

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Impact of human population density on fire frequency at the global scale

Biogeosciences Discussions ◽

10.5194/bgd-10-15735-2013 ◽

2013 ◽

Vol 10 (10) ◽

pp. 15735-15778 ◽

Cited By ~ 7

Author(s):

W. Knorr ◽

T. Kaminski ◽

A. Arneth ◽

U. Weber

Keyword(s):

Population Density ◽

Human Population ◽

Global Analysis ◽

Global Scale ◽

Fire Frequency ◽

System Component ◽

Burned Area ◽

Data Set ◽

In Fire ◽

The Impact

Abstract. Human impact on wildfires, a major Earth system component, remains poorly understood. While local studies have found more fires close to settlements and roads, assimilated charcoal records and analyses of regional fire patterns from remote-sensing observations point to a decline in fire frequency with increasing human population. Here, we present a global analysis using three multi-year satellite-based burned-area products combined with a parameter estimation and uncertainty analysis with a non-linear model. We show that at the global scale, the impact of increasing population density is mainly to reduce fire frequency. Only for areas with up to 0.1 people per km2, we find that fire frequency increases by 10 to 20% relative to its value at no population. The results are robust against choice of burned-area data set, and indicate that at only very few places on Earth, fire frequency is limited by human ignitions. Applying the results to historical population estimates results in a moderate but accelerating decline of global burned area by around 14% since 1800, with most of the decline since 1950.

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Pattern and Trend of Land Surface Temperature Change on New Guinea Island

Pertanika Journal of Science and Technology ◽

10.47836/pjst.28.4.20 ◽

2020 ◽

Vol 28 (4) ◽

Author(s):

Munawar ◽

Tofan Agung Eka Prasetya ◽

Rhysa McNeil ◽

Rohana Jani

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

New Guinea ◽

Global Scale ◽

Surface Temperature Change ◽

Sea Levels ◽

Moderate Resolution Imaging Spectroradiometer ◽

Rising Sea Levels ◽

The Many

Global warming will have an impact on nature in many ways, including rising sea levels and an increasing spread of infectious diseases. Land surface temperature is one of the many indicators that can be used to measure climate change on both a local and global scale. This study aims to analyze the change in land surface temperatures on New Guinea Island using a cubic spline method, autoregressive model, and multivariate regression. New Guinea Island was divided into 5 regions each consisting of 9 subregions. The data of each subregion was obtained from the National Aeronautics and Space Administration moderate resolution imaging spectroradiometer database from 2000 to 2019. The average change in temperature was +0.012°C per decade. However, the changes differed by region; significantly decreasing in the northwest at -0.107°C per decade (95% CI: -0.207, -0.007), significantly increasing in the south at 0.201°C per decade (95% CI: 0.069, 0.333), and remaining stable in the centralnorth, southeast and northeast.

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MODIS Sensor Capability to Burned Area Mapping—Assessment of Performance and Improvements Provided by the Latest Standard Products in Boreal Regions

Sensors ◽

10.3390/s20185423 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5423

Author(s):

José A. Moreno-Ruiz ◽

José R. García-Lázaro ◽

Manuel Arbelo ◽

Manuel Cantón-Garbín

Keyword(s):

Accuracy Assessment ◽

Fire Service ◽

Global Scale ◽

Burned Area ◽

Commission Error ◽

Positional Accuracy ◽

Wildfire Management ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

This paper presents an accuracy assessment of the main global scale Burned Area (BA) products, derived from daily images of the Moderate-Resolution Imaging Spectroradiometer (MODIS) Fire_CCI 5.1 and MCD64A1 C6, as well as the previous versions of both products (Fire_CCI 4.1 and MCD45A1 C5). The exercise was conducted on the boreal region of Alaska during the period 2000–2017. All the BA polygons registered by the Alaska Fire Service were used as reference data. Both new versions doubled the annual BA estimate compared to the previous versions (66% for Fire_CCI 5.1 versus 35% for v4.1, and 63% for MCD64A1 C6 versus 28% for C5), reducing the omission error (OE) by almost one half (39% versus 67% for Fire_CCI and 48% versus 74% for MCD) and slightly increasing the commission error (CE) (7.5% versus 7% for Fire_CCI and 18% versus 7% for MCD). The Fire_CCI 5.1 product (CE = 7.5%, OE = 39%) presented the best results in terms of positional accuracy with respect to MCD64A1 C6 (CE = 18%, OE = 48%). These results suggest that Fire_CCI 5.1 could be suitable for those users who employ BA standard products in geoinformatics analysis techniques for wildfire management, especially in Boreal regions. The Pareto boundary analysis, performed on an annual basis, showed that there is still a potential theoretical capacity to improve the MODIS sensor-based BA algorithms.

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Product dependency of Fire-driven surface albedo radiative forcing global estimates: a spatial and temporal consistency analysis

10.5194/egusphere-egu2020-9222 ◽

2020 ◽

Author(s):

Bernardo Mota ◽

Nadine Gobron ◽

Christian Lanconelli ◽

Fabrizio Capucci

Keyword(s):

Land Surface ◽

Radiative Forcing ◽

Surface Albedo ◽

Temporal Trends ◽

Model Space ◽

Earth Observation ◽

Temporal Consistency ◽

Burnt Area ◽

Albedo Change ◽

Global Land

This paper addresses the product consistency in a cross-ECV model space driven ECV&#8217;s to estimate the radiative forcing (RF) due to the direct effect of fire- driven surface albedo change. Monthly radiative forcing&#8217;s are modeled using three Earth Observation land surface albedo (MCD43C3, GlobAlbedo and Copernicus Global Land Services) and five burnt area (FireCCIv4, FireCCIv5, MCD45C5, MCD64C6 and Copernicus Global Land Services) products, and the ERA5 downward Solar radiation at the Surface. The ensemble consistency is analyzed spatially and seasonally by vegetation cover type using the Land Cover CCI product, and using four spatial resolutions (0.05&#176;, 0.10&#176;, 025&#176; and 0.5&#176;). Results show that depending on the combined products and spatial resolution, estimates can differ significantly. In general, higher estimates result at coarser resolutions and variation between product combinations can differ between 26% to 46%, depending on the type of vegetation. In addition, significant temporal trends of opposing signs can be detected. This study presents an example of cross-ECV modelling. Due to the increasing number, and coverage, of Earth Observation satellite programs, these results highlight the need to assess the fitness for purpose of the derived products.

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On the Nature of Soil Moisture in Land Surface Models

Journal of Climate ◽

10.1175/2009jcli2832.1 ◽

2009 ◽

Vol 22 (16) ◽

pp. 4322-4335 ◽

Cited By ~ 308

Author(s):

Randal D. Koster ◽

Zhichang Guo ◽

Rongqian Yang ◽

Paul A. Dirmeyer ◽

Kenneth Mitchell ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Global Scale ◽

Surface Model ◽

Land Surface Models ◽

Common Information ◽

Surface Models ◽

Moisture State ◽

Environmental Prediction

Abstract The soil moisture state simulated by a land surface model is a highly model-dependent quantity, meaning that the direct transfer of one model’s soil moisture into another can lead to a fundamental, and potentially detrimental, inconsistency. This is first illustrated with two recent examples, one from the National Centers for Environmental Prediction (NCEP) involving seasonal precipitation forecasting and another from the realm of ecological modeling. The issue is then further addressed through a quantitative analysis of soil moisture contents produced as part of a global offline simulation experiment in which a number of land surface models were driven with the same atmospheric forcing fields. These latter comparisons clearly demonstrate, on a global scale, the degree to which model-simulated soil moisture variables differ from each other and that these differences extend beyond those associated with model-specific layer thicknesses or soil texture. The offline comparisons also show, however, that once the climatological statistics of each model’s soil moisture variable are accounted for (here, through a simple scaling using the first two moments), the different land models tend to produce very similar information on temporal soil moisture variability in most parts of the world. This common information can perhaps be used as the basis for successful mappings between the soil moisture variables in different land models.

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Estimation of Consistent Global Microwave Land Surface Emissivity from AMSR-E and AMSR2 Observations

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-17-0213.1 ◽

2018 ◽

Vol 57 (4) ◽

pp. 907-919 ◽

Cited By ~ 9

Author(s):

Satya Prakash ◽

Hamid Norouzi ◽

Marzi Azarderakhsh ◽

Reginald Blake ◽

Catherine Prigent ◽

...

Keyword(s):

Skin Temperature ◽

Land Surface ◽

Weather Prediction ◽

Numerical Weather Prediction Model ◽

Passive Microwave ◽

Accurate Estimation ◽

Surface Emissivity ◽

Land Surface Emissivity ◽

Earth Observing System ◽

Moderate Resolution Imaging Spectroradiometer

AbstractAccurate estimation of passive microwave land surface emissivity (LSE) is crucial for numerical weather prediction model data assimilation, for microwave retrievals of land precipitation and atmospheric profiles, and for a better understanding of land surface and subsurface characteristics. In this study, global instantaneous LSE is estimated for a 9-yr period from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and for a 5-yr period from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors. Estimates of LSE from both sensors were obtained by using an updated algorithm that minimizes the discrepancy between the differences in penetration depths from microwave and infrared remote sensing observations. Concurrent ancillary datasets such as skin temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) and profiles of air temperature and humidity from the Atmospheric Infrared Sounder are used. The latest collection 6 of MODIS skin temperature is used for the LSE estimation, and the differences between collections 6 and 5 are also comprehensively assessed. Analyses reveal that the differences between these two versions of infrared-based skin temperatures could lead to approximately a 0.015 difference in passive microwave LSE values, especially in arid regions. The comparison of global mean LSE features from the combined use of AMSR-E and AMSR2 with an independent product—Tool to Estimate Land Surface Emissivity from Microwave to Submillimeter Waves (TELSEM2)—shows spatial pattern correlations of order 0.92 at all frequencies. However, there are considerable differences in magnitude between these two LSE estimates, possibly because of differences in incidence angles, frequencies, observation times, and ancillary datasets.

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