Linking Climate Change to Land Surface Change

Abstract. Global circulation models (GCMs) are the best tool to understand climate change, as they attempt to represent all the important Earth system processes, including anthropogenic perturbation through fossil fuel burning. However, GCMs are computationally very expensive, which limits the number of simulations that can be made. Pattern scaling is an emulation technique that takes advantage of the fact that local and seasonal changes in surface climate are often approximately linear in the rate of warming over land and across the globe. This allows interpolation away from a limited number of available GCM simulations, to assess alternative future emissions scenarios. In this paper, we present a climate pattern-scaling set consisting of spatial climate change patterns along with parameters for an energy-balance model that calculates the amount of global warming. The set, available for download, is derived from 22 GCMs of the WCRP CMIP3 database, setting the basis for similar eventual pattern development for the CMIP5 and forthcoming CMIP6 ensemble. Critically, it extends the use of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) framework to enable scanning across full uncertainty in GCMs for impact studies. Across models, the presented climate patterns represent consistent global mean trends, with a maximum of 4 (out of 22) GCMs exhibiting the opposite sign to the global trend per variable (relative humidity). The described new climate regimes are generally warmer, wetter (but with less snowfall), cloudier and windier, and have decreased relative humidity. Overall, when averaging individual performance across all variables, and without considering co-variance, the patterns explain one-third of regional change in decadal averages (mean percentage variance explained, PVE, 34.25±5.21), but the signal in some models exhibits much more linearity (e.g. MIROC3.2(hires): 41.53) than in others (GISS_ER: 22.67). The two most often considered variables, near-surface temperature and precipitation, have a PVE of 85.44±4.37 and 14.98±4.61, respectively. We also provide an example assessment of a terrestrial impact (changes in mean runoff) and compare projections by the IMOGEN system, which has one land surface model, against direct GCM outputs, which all have alternative representations of land functioning. The latter is noted as an additional source of uncertainty. Finally, current and potential future applications of the IMOGEN version 2.0 modelling system in the areas of ecosystem modelling and climate change impact assessment are presented and discussed.

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Pastoralists-farmers’ conflicts in Nigeria’s mid-Benue Trough: Socio-ecological drivers and pathways to addressing the conflicts.

10.5194/egusphere-egu21-13643 ◽

2021 ◽

Author(s):

Chukwudi Njoku ◽

Francis Okpiliya ◽

Joel Efiong ◽

Chinwe Ifejika Speranza

Keyword(s):

Climate Change ◽

Land Use ◽

Land Surface ◽

Land Development ◽

Policy Formulation ◽

Landsat 8 ◽

Land Resource ◽

Hotspot Analysis ◽

Benue Trough ◽

Year 2000

Violent conflicts related to pastoralists-farmers&#8217; interactions in Nigeria have assumed an unprecedented dimension, causing loss of lives and livelihoods. The mid-Benue trough (Benue and Taraba States) has suffered most from the conflicts. This study aims to provide knowledge on the socio-ecological drivers of pastoralists-farmers&#8217; conflicts in the mid-Benue trough from the year 2000 to 2020 and to identify pathways to solving them. First, data from the Armed Conflict Location and Event Data Project were used to map the conflicts. Second, to understand the nexus of climate change, land use and the conflicts, the study analyzed satellite data of Land Surface Temperature (LST) as a proxy for climate change, using data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite and Land Use Land Cover (LULC), using LandSat 7 ETM and LandSat 8 ETM+ data, then linked them to the mapped conflicts. Third, to understand causes and impacts of the conflict on pastoralists and farmers&#8217; livelihoods, 100 interviews were conducted, 50 for each group and analyzed using content analysis and descriptive statistics. Results showed that there were 2532 fatalities from 309 conflict events between pastoralists and farmers. The incidents exhibited statistically significant clustering and were minimal between the year 2000 and 2012, increasing gradually until the year 2013 when it began to rise geometrically. The Getis-Ord Gi hotspot analysis revealed the conflict hotspots to include Agatu, Oturkpo, Gwer East and Gashaka Local Government Areas. The results from the LST analysis showed that the area coverage of high LST increased from 30 percent in 2000 to 38 percent in 2020, while extremely high LST area also increased from 14 to 16 percent. A significantly high percentage of the conflicts (87 percent) occurred in areas with high LST (>30&#8304;C). In addition, the LULC analyses showed that built-up land area increased by 35 km2 (0.1 percent) and dense forests reduced by 798 km2 (0.1 percent). Notably, shrublands and grasslands, which are the resource domains of the pastoralists reduced by 11,716 km2&#160; (13.1 percent) and croplands of farmers increased by 12,316 km2 (13.8 percent). This presents an apparent transition of LULC from shrublands and grasslands to croplands in the area. Further analyses showed that 63 percent of the conflicts occurred in croplands and 16 percent in shrublands and grasslands. Hence, the reduction of land resource available to pastoralists and their subsequent cropland encroachment were identified as major causes of the conflict. It was therefore concluded that land development for other purposes is a major driver of pastoralists-farmers&#8217; conflicts in the study area. There is thus a need to integrate conflict maps, LST and LULC dynamics to support dialogue, land use planning and policy formulation for sustainable land management to guide pastoral and farming activities.

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Deriving SAR Soil Moisture Retrieval Algorithms and Soil Drainage Classification for Boreal Peatlands

10.5194/egusphere-egu21-6503 ◽

2021 ◽

Author(s):

Laura Bourgeau-Chavez ◽

Jeremy Graham ◽

Andrew Poley ◽

Dorthea Leisman ◽

Michael Battaglia

Keyword(s):

Climate Change ◽

Soil Moisture ◽

Land Surface ◽

Ground Surface ◽

Terrestrial Ecosystem ◽

Independent Study ◽

Additive Models ◽

Soil Drainage ◽

Boreal Peatlands ◽

Retrieval Algorithms

Eighty percent of global peatlands are distributed across the boreal and subarctic regions, storing an estimated 30% of earth&#8217;s soil organic carbon (1,016 to 1,105 Gt C) despite representing only about 3% of the global land surface. The accumulation of C in peatlands generally depends on hydrologic conditions that maintain saturated soils and impede rates of decomposition. Boreal Peatlands have provided rich reservoirs of stored C for millennia. However, with climate change, warming and drying patterns across the boreal and arctic are resulting in dramatic changes in ecosystems and putting these systems at risk of changing from a C sink to a source.&#160; Recent changes in climate including earlier springs, longer summers and changes in moisture patterns across the landscape, are affecting wildfire regimes of the boreal region including intensity, severity and frequency of wildfires. This in turn has potential to cause shifts in successional trajectories.&#160; Understanding how these changes in climate are affecting peatlands and their vulnerability to wildfire has been a focus of study of the research team since 2009. &#160;Soil moisture is one variable which can provide information to understand wildfire behavior including the depth of peat consumption in these wildfires but it also has a direct effect on post-fire successional trajectories. Further it is needed to understand methane emissions from peatlands.&#160; To develop the soil moisture retrieval algorithms, we studied a range of boreal peatland sites (bogs and fens) stratified across geographic regions from 2012-2014.&#160; We developed soil moisture retrieval algorithms from polarimetric C-band (5.7 cm wavelength) synthetic aperture radar (SAR) data.&#160; Peatlands have low enough aboveground biomass (<3.0 kg/m2) to allow this shorter wavelength SAR to penetrate the canopy to reach the ground surface.&#160; Data from over 60, 4 ha sites were collected over 3 seasons from Alaska and Michigan USA and Alberta Canada.&#160; Both multi-linear regressions and general additive models (GAM) were developed.&#160; Using both polarimetric SAR parameters that are sensitive to vegetation structure and parameters most sensitive to surface soil moisture in the models provided the best results.&#160; GAM models were tested in an independent study area, Northwest Territories (NWT), Canada.&#160; The sites of NWT were sampled in 2016-2019 coincident to Radarsat-2 polarimetric image collections.&#160; The high accuracy results will be presented as well as methods developed to use multidate C-band data from Sentinel-1 to classify soil drainage (well drained to poorly drained) in recently burned peatlands.&#160; These products are being used in a fire effects and emissions model, CanFIRE, as we parameterize it for peatlands; as well as the Functionally-Assembled Terrestrial Ecosystem Simulator (FATES) to understand the effects of wildfire and hydrology on peatland ecosystems.&#160; Characterization and quantification of boreal peatlands in global C cycling is critical for proper accounting given that peatlands play a significant role in sequestering and releasing large amounts of C. The ability to retrieve soil moisture from C-band SAR, therefore, provides a means to monitor a key variable in scaling C flux estimates as well as understanding the vulnerability and resiliency of boreal peatlands to climate change.&#160;

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Fire regime impacts on post-fire diurnal land surface temperature change over North American boreal forest

10.5194/egusphere-egu21-10702 ◽

2021 ◽

Author(s):

Jie Zhao ◽

Chao Yue ◽

Philippe Ciais ◽

Xin Hou ◽

Qi Tian

Keyword(s):

Climate Change ◽

Regression Analysis ◽

Land Surface ◽

Fire Regime ◽

Fire Severity ◽

Linear Regression Analysis ◽

Fire Regimes ◽

Fire Intensity ◽

Surface Temperature Change ◽

One Year

Wildfire is the most prevalent natural disturbance in the North American boreal (BNA) forest and can cause post-fire land surface temperature change (&#916;LSTfire) through biophysical processes. Fire regimes, such as fire severity, fire intensity and percentage of burned area (PBA), might affect &#916;LSTfire through their impacts on post-fire vegetation damage. However, the difference of the influence of different fire regimes on the &#916;LSTfire has not been quantified in previous studies, despite ongoing and projected changes in fire regimes in BNA in association with climate change. Here we employed satellite observations and a space-and-time approach to investigate diurnal &#916;LSTfire one year after fire across BNA. We further examined potential impacts of three fire regimes (i.e., fire intensity, fire severity and PBA) and latitude on &#916;LSTfire by simple linear regression analysis and multiple linear regression analysis in a stepwise manner. Our results demonstrated pronounced asymmetry in diurnal &#916;LSTfire, characterized by daytime warming in contrast to nighttime cooling over most BNA. Such diurnal &#916;LSTfire also exhibits a clear latitudinal pattern, with stronger daytime warming and nighttime cooling one year after fire in lower latitudes, whereas in high latitudes fire effects are almost neutral. Among the fire regimes, fire severity accounted for the most (43.65%) of the variation of daytime &#916;LSTfire, followed by PBA (11.6%) and fire intensity (8.5%). The latitude is an important factor affecting the influence of fire regimes on daytime &#916;LSTfire. The sensitivity of fire intensity and PBA impact on daytime &#916;LSTfire decreases with latitude. But only fire severity had a significant effect on nighttime &#916;LSTfire among three fire regimes. Our results highlight important fire regime impacts on daytime &#916;LSTfire, which might play a critical role in catalyzing future boreal climate change through positive feedbacks between fire regime and post-fire surface warming.

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