Parameter uncertainty dominates C-cycle forecast  errors over most of Brazil for the 21st century

Abstract. Identification of terrestrial carbon (C) sources and sinks is critical for understanding the Earth system as well as mitigating and adapting to climate change resulting from greenhouse gas emissions. Predicting whether a given location will act as a C source or sink using terrestrial ecosystem models (TEMs) is challenging due to net flux being the difference between far larger, spatially and temporally variable fluxes with large uncertainties. Uncertainty in projections of future dynamics, critical for policy evaluation, has been determined using multi-TEM intercomparisons, for various emissions scenarios. This approach quantifies structural and forcing errors. However, the role of parameter error within models has not been determined. TEMs typically have defined parameters for specific plant functional types generated from the literature. To ascertain the importance of parameter error in forecasts, we present a Bayesian analysis that uses data on historical and current C cycling for Brazil to parameterise five TEMs of varied complexity with a retrieval of model error covariance at 1∘ spatial resolution. After evaluation against data from 2001–2017, the parameterised models are simulated to 2100 under four climate change scenarios spanning the likely range of climate projections. Using multiple models, each with per pixel parameter ensembles, we partition forecast uncertainties. Parameter uncertainty dominates across most of Brazil when simulating future stock changes in biomass C and dead organic matter (DOM). Uncertainty of simulated biomass change is most strongly correlated with net primary productivity allocation to wood (NPPwood) and mean residence time of wood (MRTwood). Uncertainty of simulated DOM change is most strongly correlated with MRTsoil and NPPwood. Due to the coupling between these variables and C stock dynamics being bi-directional, we argue that using repeat estimates of woody biomass will provide a valuable constraint needed to refine predictions of the future carbon cycle. Finally, evaluation of our multi-model analysis shows that wood litter contributes substantially to fire emissions, necessitating a greater understanding of wood litter C cycling than is typically considered in large-scale TEMs.

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Parameter uncertainty dominates C cycle forecast errors over most of Brazil for the 21st Century

10.5194/esd-2021-17 ◽

2021 ◽

Author(s):

Thomas Luke Smallman ◽

David Thomas Milodowski ◽

Eráclito Sousa Neto ◽

Gerbrand Koren ◽

Jean Ometto ◽

...

Keyword(s):

Climate Change ◽

Parameter Uncertainty ◽

Climate Projections ◽

Forecast Errors ◽

Climate Change Scenarios ◽

Strongly Correlated ◽

Fire Emissions ◽

C Cycling ◽

C Cycle ◽

Parameter Error

Abstract. Identification of terrestrial carbon (C) sources and sinks is critical for understanding the earth system and to mitigate and adapt to climate change results from greenhouse gas emissions. Predicting whether a given location will act as a C source or sink using terrestrial ecosystem models (TEMs) is challenging due to net flux being the difference between far larger, spatially and temporally variable fluxes with large uncertainties. Uncertainty in projections of future dynamics, critical for policy evaluation, has been determined using multi-TEM intercomparisons, for various emissions scenarios. This approach quantifies structural and forcing errors. However, the role of parameter error within models has not been determined. TEMs typically have defined parameters for specific plant functional types generated from the literature. To ascertain the importance of parameter error in forecasts we present a Bayesian analysis that uses data on historical and current C cycling for Brazil to parameterise five TEMs of varied complexity with a retrieval of model error covariance at 1 degree spatial resolution. After evaluation against data from 2001–2017, the parameterised models are simulated to 2100 under four climate change scenarios spanning the likely range of climate projections. Using multiple models, each with per pixel parameter ensembles, we partition forecast uncertainties. Parameter uncertainty dominates across most of Brazil when simulating future stock changes in biomass C and dead organic matter (DOM). Uncertainty of simulated biomass change is most strongly correlated with net primary productivity allocation to wood (NPPwood) and wood mean residence times (MRTwood). Uncertainty of simulated DOM change is most strongly correlated with MRTsoil and NPPwood. Due to the coupling between these variables and C stock dynamics being bi-directional we argue that using repeat estimates of woody biomass will provide a valuable constraint needed to refine predictions of the future carbon cycle. Finally, evaluation of our multi-model analysis shows that wood litter contributes substantially to fire emissions necessitating a greater understanding of wood litter C-cycling than is typically considered in large-scale TEMs.

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Urban Geometry Optimization to Mitigate Climate Change: Towards Energy-Efficient Buildings

Sustainability ◽

10.3390/su13010027 ◽

2020 ◽

Vol 13 (1) ◽

pp. 27

Author(s):

Hatem Mahmoud ◽

Ayman Ragab

Keyword(s):

Climate Change ◽

Heat Stress ◽

Thermal Efficiency ◽

Energy Demand ◽

Building Blocks ◽

Climate Projections ◽

Climate Change Scenarios ◽

Urban Geometry ◽

Outdoor Spaces ◽

The Impact

The density of building blocks and insufficient greenery in cities tend to contribute dramatically not only to increased heat stress in the built environment but also to higher energy demand for cooling. Urban planners should, therefore, be conscious of their responsibility to reduce energy usage of buildings along with improving outdoor thermal efficiency. This study examines the impact of numerous proposed urban geometry cases on the thermal efficiency of outer spaces as well as the energy consumption of adjacent buildings under various climate change scenarios as representative concentration pathways (RCP) 4.5 and 8.5 climate projections for New Aswan city in 2035. The investigation was performed at one of the most underutilized outdoor spaces on the new campus of Aswan University in New Aswan city. The potential reduction of heat stress was investigated so as to improve the thermal comfort of the investigated outdoor spaces, as well as energy savings based on the proposed strategies. Accordingly, the most appropriate scenario to be adopted to cope with the inevitable climate change was identified. The proposed scenarios were divided into four categories of parameters. In the first category, shelters partially (25–50% and 75%) covering the streets were used. The second category proposed dividing the space parallel or perpendicular to the existing buildings. The third category was a hybrid scenario of the first and second categories. In the fourth category, a green cover of grass was added. A coupling evaluation was applied utilizing ENVI-met v4.2 and Design-Builder v4.5 to measure and improve the thermal efficiency of the outdoor space and reduce the cooling energy. The results demonstrated that it is better to cover outdoor spaces with 50% of the overall area than transform outdoor spaces into canyons.

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Equilibrium Spin-up of Cold and Warm Permafrost Models

10.5194/egusphere-egu21-13922 ◽

2021 ◽

Author(s):

Cameron Ross ◽

Ryley Beddoe ◽

Greg Siemens

Keyword(s):

Climate Change ◽

Temperature Response ◽

Ground Temperature ◽

Future Climate Change ◽

Climate Projections ◽

Multiple Model ◽

Climate Data ◽

Ground Temperatures ◽

C Cycle ◽

Warm Permafrost

Initialization (spin-up) of a numerical ground temperature model is a critical but often neglected step for solving heat transfer problems in permafrost. Improper initialization can lead to significant underlying model drift in subsequent transient simulations, distorting the effects on ground temperature from future climate change or applied infrastructure. &#160;In a typical spin-up simulation, a year or more of climate data are applied at the surface and cycled repeatedly until ground temperatures are declared to be at equilibrium with the imposed boundary conditions, and independent of the starting conditions.Spin-up equilibrium is often simply declared after a specified number of spin-up cycles. In few studies, equilibrium is visually confirmed by plotting ground temperatures vs spin-up cycles until temperatures stabilize; or is declared when a certain inter-cycle-temperature-change threshold is met simultaneously at all depths, such as &#8710;T &#8804; 0.01oC per cycle. In this study, we investigate the effectiveness of these methods for determining an equilibrium state in a variety of permafrost models, including shallow and deep (10 &#8211; 200 m), high and low saturation soils (S = 100 and S = 20), and cold and warm permafrost (MAGT = ~-10 oC and >-1 oC). The efficacy of equilibrium criteria 0.01oC/cycle and 0.0001oC/cycle are compared. Both methods are shown to prematurely indicate equilibrium in multiple model scenarios. &#160;Results show that no single criterion can programmatically detect equilibrium in all tested models, and in some scenarios can result in up to 10oC temperature error or 80% less permafrost than at true equilibrium. &#160;A combination of equilibrium criteria and visual confirmation plots is recommended for evaluating and declaring equilibrium in a spin-up simulation.Long-duration spin-up is particularly important for deep (10+&#160;m) ground models where thermal inertia of underlying permafrost slows the ground temperature response to surface forcing, often requiring hundreds or even thousands of spin-up cycles to establish equilibrium. Subsequent transient analyses also show that use of a properly initialized 100 m permafrost model can reduce the effect of climate change on mean annual ground temperature of cold permafrost by more than 1 oC and 3 oC under RCP2.6 and RCP8.5 climate projections, respectively, when compared to an identical 25 m model. These results have important implications for scientists, engineers and policy makers that rely on model projections of long-term permafrost conditions.

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Simulating future precipitation extremes in a complex Alpine catchment

Natural Hazards and Earth System Science ◽

10.5194/nhess-13-263-2013 ◽

2013 ◽

Vol 13 (2) ◽

pp. 263-277 ◽

Cited By ~ 9

Author(s):

C. Dobler ◽

G. Bürger ◽

J. Stötter

Keyword(s):

Climate Change ◽

Large Scale ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Precipitation Extremes ◽

Climate Projections ◽

Research Station ◽

Climate Change Scenarios ◽

Station Data

Abstract. The objectives of the present investigation are (i) to study the effects of climate change on precipitation extremes and (ii) to assess the uncertainty in the climate projections. The investigation is performed on the Lech catchment, located in the Northern Limestone Alps. In order to estimate the uncertainty in the climate projections, two statistical downscaling models as well as a number of global and regional climate models were considered. The downscaling models applied are the Expanded Downscaling (XDS) technique and the Long Ashton Research Station Weather Generator (LARS-WG). The XDS model, which is driven by analyzed or simulated large-scale synoptic fields, has been calibrated using ECMWF-interim reanalysis data and local station data. LARS-WG is controlled through stochastic parameters representing local precipitation variability, which are calibrated from station data only. Changes in precipitation mean and variability as simulated by climate models were then used to perturb the parameters of LARS-WG in order to generate climate change scenarios. In our study we use climate simulations based on the A1B emission scenario. The results show that both downscaling models perform well in reproducing observed precipitation extremes. In general, the results demonstrate that the projections are highly variable. The choice of both the GCM and the downscaling method are found to be essential sources of uncertainty. For spring and autumn, a slight tendency toward an increase in the intensity of future precipitation extremes is obtained, as a number of simulations show statistically significant increases in the intensity of 90th and 99th percentiles of precipitation on wet days as well as the 5- and 20-yr return values.

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Ensemble Projections of Regional Climatic Changes over Ontario, Canada

Journal of Climate ◽

10.1175/jcli-d-15-0185.1 ◽

2015 ◽

Vol 28 (18) ◽

pp. 7327-7346 ◽

Cited By ~ 29

Author(s):

Xiuquan Wang ◽

Guohe Huang ◽

Jinliang Liu ◽

Zhong Li ◽

Shan Zhao

Keyword(s):

Climate Change ◽

Global Warming ◽

Regional Climate ◽

Climatic Changes ◽

Climate Projections ◽

Climate Change Scenarios ◽

Local Climate ◽

Ensemble Simulations ◽

Extreme Precipitation Events ◽

Data Portal

Abstract In this study, high-resolution climate projections over Ontario, Canada, are developed through an ensemble modeling approach to provide reliable and ready-to-use climate scenarios for assessing plausible effects of future climatic changes at local scales. The Providing Regional Climates for Impacts Studies (PRECIS) regional modeling system is adopted to conduct ensemble simulations in a continuous run from 1950 to 2099, driven by the boundary conditions from a HadCM3-based perturbed physics ensemble. Simulations of temperature and precipitation for the baseline period are first compared to the observed values to validate the performance of the ensemble in capturing the current climatology over Ontario. Future projections for the 2030s, 2050s, and 2080s are then analyzed to help understand plausible changes in its local climate in response to global warming. The analysis indicates that there is likely to be an obvious warming trend with time over the entire province. The increase in average temperature is likely to be varying within [2.6, 2.7]°C in the 2030s, [4.0, 4.7]°C in the 2050s, and [5.9, 7.4]°C in the 2080s. Likewise, the annual total precipitation is projected to increase by [4.5, 7.1]% in the 2030s, [4.6, 10.2]% in the 2050s, and [3.2, 17.5]% in the 2080s. Furthermore, projections of rainfall intensity–duration–frequency (IDF) curves are developed to help understand the effects of global warming on extreme precipitation events. The results suggest that there is likely to be an overall increase in the intensity of rainfall storms. Finally, a data portal named Ontario Climate Change Data Portal (CCDP) is developed to ensure decision-makers and impact researchers have easy and intuitive access to the refined regional climate change scenarios.

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Impact of climate change on hydro-meteorological drought over the Be River Basin, Viet Nam

Journal of Water and Climate Change ◽

10.2166/wcc.2021.137 ◽

2021 ◽

Author(s):

Dao Nguyen Khoi ◽

Truong Thao Sam ◽

Pham Thi Loi ◽

Bui Viet Hung ◽

Van Thinh Nguyen

Keyword(s):

Climate Change ◽

River Basin ◽

General Circulation ◽

Southern Oscillation ◽

Meteorological Drought ◽

Climate Projections ◽

Research Station ◽

Climate Change Scenarios ◽

Study Region ◽

Meteorological Droughts

Abstract In this paper, the responses of hydro-meteorological drought to changing climate in the Be River Basin located in Southern Vietnam are investigated. Climate change scenarios for the study area were statistically downscaled using the Long Ashton Research Station Weather Generator tool, which incorporates climate projections from Coupled Model Intercomparison Project 5 (CMIP5) based on an ensemble of five general circulation models (Can-ESM2, CNRM-CM5, HadGEM2-AO, IPSL-CM5A-LR, and MPI-ESM-MR) under two Representative Concentration Pathway (RCP) scenarios (RCP4.5 and RCP8.5). The Soil and Water Assessment Tool model was employed to simulate streamflow for the baseline time period and three consecutive future 20 year periods of 2030s (2021–2040), 2050s (2041–2060), and 2070s (2061–2080). Based on the simulation results, the Standardized Precipitation Index and Standardized Discharge Index were estimated to evaluate the features of hydro-meteorological droughts. The hydrological drought has 1-month lag time from the meteorological drought and the hydro-meteorological droughts have negative correlations with the El Niño Southern Oscillation and Pacific Decadal Oscillation. Under the climate changing impacts, the trends of drought severity will decrease in the future; while the trends of drought frequency will increase in the near future period (2030s), but decrease in the following future periods (2050 and 2070s). The findings of this study can provide useful information to the policy and decisionmakers for a better future planning and management of water resources in the study region.

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Modeling the Impacts of Climate Change on Crop Yield and Irrigation in the Monocacy River Watershed, USA

Climate ◽

10.3390/cli8120139 ◽

2020 ◽

Vol 8 (12) ◽

pp. 139

Author(s):

Manashi Paul ◽

Sijal Dangol ◽

Vitaly Kholodovsky ◽

Amy R. Sapkota ◽

Masoud Negahban-Azar ◽

...

Keyword(s):

Climate Change ◽

Crop Yield ◽

Crop Yields ◽

Swat Model ◽

Global Climate Models ◽

Climate Projections ◽

Climate Change Scenarios ◽

River Watershed ◽

Temperature And Precipitation ◽

The Impact

Crop yield depends on multiple factors, including climate conditions, soil characteristics, and available water. The objective of this study was to evaluate the impact of projected temperature and precipitation changes on crop yields in the Monocacy River Watershed in the Mid-Atlantic United States based on climate change scenarios. The Soil and Water Assessment Tool (SWAT) was applied to simulate watershed hydrology and crop yield. To evaluate the effect of future climate projections, four global climate models (GCMs) and three representative concentration pathways (RCP 4.5, 6, and 8.5) were used in the SWAT model. According to all GCMs and RCPs, a warmer climate with a wetter Autumn and Spring and a drier late Summer season is anticipated by mid and late century in this region. To evaluate future management strategies, water budget and crop yields were assessed for two scenarios: current rainfed and adaptive irrigated conditions. Irrigation would improve corn yields during mid-century across all scenarios. However, prolonged irrigation would have a negative impact due to nutrients runoff on both corn and soybean yields compared to rainfed condition. Decision tree analysis indicated that corn and soybean yields are most influenced by soil moisture, temperature, and precipitation as well as the water management practice used (i.e., rainfed or irrigated). The computed values from the SWAT modeling can be used as guidelines for water resource managers in this watershed to plan for projected water shortages and manage crop yields based on projected climate change conditions.

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Flood risk assessment methodology for planning under climate change scenarios and the corresponding change in land cover

Journal of Water and Climate Change ◽

10.2166/wcc.2019.016 ◽

2019 ◽

Vol 11 (4) ◽

pp. 1370-1382 ◽

Cited By ~ 1

Author(s):

Asma Hanif ◽

Ashwin Dhanasekar ◽

Anthony Keene ◽

Huishu Li ◽

Kenneth Carlson

Keyword(s):

Climate Change ◽

Climate Model ◽

Global Climate Model ◽

The United States ◽

Army Corps ◽

Climate Projections ◽

Climate Change Scenarios ◽

Rcp Scenarios ◽

Daily Precipitation Data ◽

Time Frames

Abstract Projected climate change impacts on the hydrological regime and corresponding flood risks were examined for the years 2030 (near-term) and 2050 (long-term), under representative concentration pathways (RCP) 4.5 (moderate) and 8.5 (high) emission scenarios. The United States Army Corps of Engineers' (USACE) Hydrologic Engineering Center's Hydrologic Modeling System was used to simulate the complete hydrologic processes of the various dendritic watershed systems and USACEs' Hydrologic Engineering Center's River Analysis System hydraulic model was used for the two-dimensional unsteady flow flood calculations. Climate projections are based on recent global climate model simulations developed for the International Panel on Climate Change, Coupled Model Inter-comparison Project Phase 5. Hydrographs for frequent (high-recurrence interval) storms were derived from 30-year historical daily precipitation data and decadal projections for both time frames and RCP scenarios. Since the climate projections for each scenario only represented ten years of data, 100-year or 500-year storms cannot be derived. Hence, this novel approach of identifying frequent storms is used as an indicator to compare across the various time frames and climate scenarios. Hydrographs were used to generate inundation maps and results are used to identify vulnerabilities and formulate adaptation strategies to flooding at 43 locations worldwide.

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Current and Future Potential Distribution of the Xerophytic Shrub Candelilla (Euphorbia antisyphilitica) under Two Climate Change Scenarios

Forests ◽

10.3390/f11050530 ◽

2020 ◽

Vol 11 (5) ◽

pp. 530 ◽

Cited By ~ 1

Author(s):

Gonzalo Vargas-Piedra ◽

Ricardo David Valdez-Cepeda ◽

Armando López-Santos ◽

Arnoldo Flores-Hernández ◽

Nathalie S. Hernández-Quiroz ◽

...

Keyword(s):

Climate Change ◽

North America ◽

Habitat Suitability ◽

Potential Distribution ◽

The United States ◽

Economic Importance ◽

Distribution Area ◽

Climate Projections ◽

Geophysical Fluid Dynamics Laboratory ◽

Climate Change Scenarios

Candelilla (Euphorbia antisyphilitica Zucc.) is a shrub species distributed throughout the Chihuahuan Desert in northern Mexico and southern of the United States of America. Candelilla has an economic importance due to natural wax it produces. The economic importance and the intense harvest of the wax from candelilla seems to gradually reduce the natural populations of this species. The essence of this research was to project the potential distribution of candelilla populations under different climate change scenarios in its natural distribution area in North America. We created a spatial database with points of candelilla presence, according to the Global Biodiversity Information Facility (GBIF). A spatial analysis to predict the potential distribution of the species using Maxent software was performed. Thirteen of 19 variables from the WorldClim database were used for two scenarios of representative concentration pathways (RCPs) (4.5 as a conservative and 8.5 as extreme). We used climate projections from three global climate models (GCMs) (Max Planck institute, the Geophysical Fluid Dynamics Laboratory and the Met Office Hadley), each simulating the two scenarios. The final predicted distribution areas were classified in five on-site possible candelilla habitat suitability categories: none (< 19%), low (20–38%), medium (39–57%), high (58–76%) and very high (> 77%). According to the area under the curve (0.970), the models and scenarios used showed an adequate fit to project the current and future distribution of candelilla. The variable that contributed the most in the three GCMs and the two RCPs was the mean temperature of the coldest quarter with an influence of 45.7% (Jackknife test). The candelilla’s distribution area for North America was predicted as approximately 19.1 million hectares under the current conditions for the high habitat suitability; however, the projection for the next fifty years is not promising because the GCMs projected a reduction of more than 6.9 million hectares using either the conservative or extreme scenarios. The results are useful for conservation of the species in the area with vulnerable wild populations, as well as for the selection of new sites suitable for the species growth and cultivation while facing climate change.

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Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks

Sustainability ◽

10.3390/su11174764 ◽

2019 ◽

Vol 11 (17) ◽

pp. 4764 ◽

Cited By ~ 7

Author(s):

Anna Sperotto ◽

Josè Luis Molina ◽

Silvia Torresan ◽

Andrea Critto ◽

Manuel Pulido-Velazquez ◽

...

Keyword(s):

Climate Change ◽

Water Resources ◽

Bayesian Networks ◽

Climate Model ◽

Future Climate Change ◽

Climate Projections ◽

Climate Change Scenarios ◽

Large Variability ◽

Nutrient Loadings

With increasing evidence of climate change affecting the quality of water resources, there is the need to assess the potential impacts of future climate change scenarios on water systems to ensure their long-term sustainability. The study assesses the uncertainty in the hydrological responses of the Zero river basin (northern Italy) generated by the adoption of an ensemble of climate projections from 10 different combinations of a global climate model (GCM)–regional climate model (RCM) under two emission scenarios (representative concentration pathways (RCPs) 4.5 and 8.5). Bayesian networks (BNs) are used to analyze the projected changes in nutrient loadings (NO3, NH4, PO4) in mid- (2041–2070) and long-term (2071–2100) periods with respect to the baseline (1983–2012). BN outputs show good confidence that, across considered scenarios and periods, nutrient loadings will increase, especially during autumn and winter seasons. Most models agree in projecting a high probability of an increase in nutrient loadings with respect to current conditions. In summer and spring, instead, the large variability between different GCM–RCM results makes it impossible to identify a univocal direction of change. Results suggest that adaptive water resource planning should be based on multi-model ensemble approaches as they are particularly useful for narrowing the spectrum of plausible impacts and uncertainties on water resources.

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