Influence of potential climate change on forest landscape dynamics of west-central Alberta

Changes in climatic conditions may influence both forest biomass accumulation rates and natural disturbance regimes. While changes in biomass accumulation of forests under various climatic conditions have been described by yield equations, large uncertainties exist with regard to disturbance regimes. Under the doubling carbon dioxide scenario, global warming impacts have been predicted from simulation results of the first generation of coupled global climate model (CGCMI). The calculated fine fuel moisture code (FFMC) distribution from the simulation results showed a one-point increase compared with the distribution under current climate conditions. The impact of predicted changes in FFMC distributions on fire disturbance patterns, forest volume, and landscape structure was investigated by using the spatially explicit model for landscape dynamics (SEM-LAND). The simulation results showed increases in fire disturbance frequency and decreases in forest volume. The simulations also showed decreases in landscape fragmentation and landscape diversity, whereas total availability of core habitat for wildlife increased.

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The Impact of Climate Change on Air Quality–Related Meteorological Conditions in California. Part I: Present Time Simulation Analysis

Journal of Climate ◽

10.1175/2011jcli3849.1 ◽

2011 ◽

Vol 24 (13) ◽

pp. 3344-3361 ◽

Cited By ~ 23

Author(s):

Zhan Zhao ◽

Shu-Hua Chen ◽

Michael J. Kleeman ◽

Mary Tyree ◽

Dan Cayan

Keyword(s):

Climate Change ◽

Air Quality ◽

Surface Temperature ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Surface Wind ◽

Valley Wind ◽

Simulation Results ◽

The Impact

Abstract This study investigates the impacts of climate change on meteorology and air quality conditions in California by dynamically downscaling Parallel Climate Model (PCM) data to high resolution (4 km) using the Weather Research and Forecast (WRF) model. This paper evaluates the present years’ (2000–06) downscaling results driven by either PCM or National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) reanalysis data. The analyses focused on the air quality–related meteorological variables, such as planetary boundary layer height (PBLH), surface temperature, and wind. The differences of the climatology from the two sets of downscaling simulations and the driving global datasets were compared, which illustrated that most of the biases of the downscaling results were inherited from the driving global climate model (GCM). The downscaling process added mesoscale features but also introduced extra biases into the driving global data. The main source of bias in the PCM data is an imprecise prediction of the location and strength of the Pacific subtropical high (PSH). The analysis implied that using simulation results driven by PCM data as the input for air quality models will likely underestimate air pollution problems in California. Regional-averaged statistics of the downscaling results were estimated for two highly polluted areas, the South Coast Air Basin (SoCAB) and the San Joaquin Valley (SJV), by comparing to observations. The simulations driven by GFS data overestimated surface temperature and wind speed for most of the year, indicating that WRF has systematic errors in these two regions. The simulation matched the observations better during summer than winter in terms of bias. WRF has difficulty reproducing weak surface wind, which normally happens during stagnation events in these two regions. The shallow summer PBLH in the Central Valley is caused by the dominance of high pressure systems over the valley and the strong valley wind during summer. The change of meteorology and air quality in California due to climate change will be explored in Part II of this study, which compares the future (2047–53) and present (2000–06) simulation results driven by PCM data and is presented in a separate paper.

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High-Resolution Climate Change Impact Analysis on Expected Future Water Availability in the Upper Jordan Catchment and the Middle East

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-0153.1 ◽

2014 ◽

Vol 15 (4) ◽

pp. 1517-1531 ◽

Cited By ~ 15

Author(s):

Gerhard Smiatek ◽

Harald Kunstmann ◽

Andreas Heckl

Keyword(s):

Climate Change ◽

High Resolution ◽

Water Availability ◽

Impact Analysis ◽

River Flow ◽

Climate Model ◽

Mesoscale Model ◽

Global Climate Model ◽

Full Range ◽

The Impact

Abstract The impact of climate change on the future water availability of the upper Jordan River (UJR) and its tributaries Dan, Snir, and Hermon located in the eastern Mediterranean is evaluated by a highly resolved distributed approach with the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) run at 18.6- and 6.2-km resolution offline coupled with the Water Flow and Balance Simulation Model (WaSiM). The MM5 was driven with NCEP reanalysis for 1971–2000 and with Hadley Centre Coupled Model, version 3 (HadCM3), GCM forcings for 1971–2099. Because only one regional–global climate model combination was applied, the results may not give the full range of possible future projections. To describe the Dan spring behavior, the hydrological model was extended by a bypass approach to allow the fast discharge components of the Snir to enter the Dan catchment. Simulation results for the period 1976–2000 reveal that the coupled system was able to reproduce the observed discharge rates in the partially karstic complex terrain to a reasonable extent with the high-resolution 6.2-km meteorological input only. The performed future climate simulations show steadily rising temperatures with 2.2 K above the 1976–2000 mean for the period 2031–60 and 3.5 K for the period 2070–99. Precipitation trends are insignificant until the middle of the century, although a decrease of approximately 12% is simulated. For the end of the century, a reduction in rainfall ranging between 10% and 35% can be expected. Discharge in the UJR is simulated to decrease by 12% until 2060 and by 26% until 2099, both related to the 1976–2000 mean. The discharge decrease is associated with a lower number of high river flow years.

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Revisiting the Krakatau 1883 Volcanic Aerosol Dispersal

10.5194/egusphere-egu21-10489 ◽

2021 ◽

Author(s):

Rafael Castro ◽

Tushar Mittal ◽

Stephen Self

Keyword(s):

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Volcanic Eruptions ◽

Climate Impact ◽

Volcanic Plume ◽

Quasi Biennial Oscillation ◽

Aerosol Cloud ◽

Pinatubo Eruption ◽

The Impact

The 1883 Krakatau eruption is one of the most well-known historical volcanic eruptions due to its significant global climate impact as well as first recorded observations of various aerosol associated optical and physical phenomena. Although much work has been done on the former by comparison of global climate model predictions/ simulations with instrumental and proxy climate records, the latter has surprisingly not been studied in similar detail. In particular, there is a wealth of observations of vivid red sunsets, blue suns, and other similar features, that can be used to analyze the spatio-temporal dispersal of volcanic aerosols in summer to winter 1883. Thus, aerosol cloud dispersal after the Krakatau eruption can be estimated, bolstered by aerosol cloud behavior as monitored by satellite-based instrument observations after the 1991 Pinatubo eruption. This is one of a handful of large historic eruptions where this analysis can be done (using non-climate proxy methods). In this study, we model particle trajectories of the Krakatau eruption cloud using the Hysplit trajectory model and compare our results with our compiled observational dataset (principally using Verbeek 1884, the Royal Society report, and Kiessling 1884).In particular, we explore the effect of different atmospheric states - the quasi-biennial oscillation (QBO) which impacts zonal movement of the stratospheric volcanic plume - to estimate the phase of the QBO in 1883 required for a fast-moving westward cloud. Since this alone is unable to match the observed latitudinal spread of the aerosols, we then explore the impact of an&#160; umbrella cloud (2000 km diameter) that almost certainly formed during such a large eruption. A large umbrella cloud, spreading over ~18 degrees within the duration of the climax of the eruption (6-8 hours), can lead to much quicker latitudinal spread than a point source (vent). We will discuss the results of the combined model (umbrella cloud and correct QBO phase) with historical accounts and observations, as well as previous work on the 1991 Pinatubo eruption. We also consider the likely impacts of water on aerosol concentrations and the relevance of this process for eruptions with possible significant seawater interactions, like Krakatau. We posit that the role of umbrella clouds is an under-appreciated, but significant, process for beginning to model the climatic impacts of large volcanic eruptions.

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Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

Geoscientific Model Development ◽

10.5194/gmd-10-1383-2017 ◽

2017 ◽

Vol 10 (3) ◽

pp. 1383-1402 ◽

Cited By ~ 35

Author(s):

Paolo Davini ◽

Jost von Hardenberg ◽

Susanna Corti ◽

Hannah M. Christensen ◽

Stephan Juricke ◽

...

Keyword(s):

High Resolution ◽

Large Scale ◽

Climate Model ◽

Global Climate ◽

Rainfall Variability ◽

Global Climate Model ◽

Small Scale ◽

Low Resolution ◽

The Impact ◽

Stochastic Physics

Abstract. The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth system model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125 up to 16 km). The project includes more than 120 simulations in both a historical scenario (1979–2008) and a climate change projection (2039–2068), together with coupled transient runs (1850–2100). A total of 20.4 million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1.5 PB of output data have been produced on SuperMUC IBM Petascale System at the Leibniz Supercomputing Centre (LRZ) in Garching, Germany. About 140 TB of post-processed data are stored on the CINECA supercomputing centre archives and are freely accessible to the community thanks to an EUDAT data pilot project. This paper presents the technical and scientific set-up of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1.0 protocol. In addition, an overview of preliminary results is given. An improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increase is observed. It is also shown that including stochastic parameterisation in the low-resolution runs helps to improve some aspects of the tropical climate – specifically the Madden–Julian Oscillation and the tropical rainfall variability. These findings show the importance of representing the impact of small-scale processes on the large-scale climate variability either explicitly (with high-resolution simulations) or stochastically (in low-resolution simulations).

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A bias-corrected CMIP5 dataset for Africa using the CDF-t method – a contribution to agricultural impact studies

Earth System Dynamics ◽

10.5194/esd-9-313-2018 ◽

2018 ◽

Vol 9 (1) ◽

pp. 313-338 ◽

Cited By ~ 18

Author(s):

Adjoua Moise Famien ◽

Serge Janicot ◽

Abe Delfin Ochou ◽

Mathieu Vrac ◽

Dimitri Defrance ◽

...

Keyword(s):

West Africa ◽

Air Temperature ◽

Climate Model ◽

Global Climate Model ◽

Cumulative Distribution ◽

Shortwave Radiation ◽

Climate Change Scenarios ◽

Reference Dataset ◽

Near Surface ◽

The Impact

Abstract. The objective of this paper is to present a new dataset of bias-corrected CMIP5 global climate model (GCM) daily data over Africa. This dataset was obtained using the cumulative distribution function transform (CDF-t) method, a method that has been applied to several regions and contexts but never to Africa. Here CDF-t has been applied over the period 1950–2099 combining Historical runs and climate change scenarios for six variables: precipitation, mean near-surface air temperature, near-surface maximum air temperature, near-surface minimum air temperature, surface downwelling shortwave radiation, and wind speed, which are critical variables for agricultural purposes. WFDEI has been used as the reference dataset to correct the GCMs. Evaluation of the results over West Africa has been carried out on a list of priority user-based metrics that were discussed and selected with stakeholders. It includes simulated yield using a crop model simulating maize growth. These bias-corrected GCM data have been compared with another available dataset of bias-corrected GCMs using WATCH Forcing Data as the reference dataset. The impact of WFD, WFDEI, and also EWEMBI reference datasets has been also examined in detail. It is shown that CDF-t is very effective at removing the biases and reducing the high inter-GCM scattering. Differences with other bias-corrected GCM data are mainly due to the differences among the reference datasets. This is particularly true for surface downwelling shortwave radiation, which has a significant impact in terms of simulated maize yields. Projections of future yields over West Africa are quite different, depending on the bias-correction method used. However all these projections show a similar relative decreasing trend over the 21st century.

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Assessing the impact of global warming on worldwide open field tomato cultivation through CSIRO-Mk3·0 global climate model

The Journal of Agricultural Science ◽

10.1017/s0021859616000654 ◽

2016 ◽

Vol 155 (3) ◽

pp. 407-420 ◽

Cited By ~ 7

Author(s):

R. S. SILVA ◽

L. KUMAR ◽

F. SHABANI ◽

M. C. PICANÇO

Keyword(s):

Climate Change ◽

Global Warming ◽

Open Field ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Current Model ◽

Global Climate Models ◽

The Impact

SUMMARYTomato (Solanum lycopersicum L.) is one of the most important vegetable crops globally and an important agricultural sector for generating employment. Open field cultivation of tomatoes exposes the crop to climatic conditions, whereas greenhouse production is protected. Hence, global warming will have a greater impact on open field cultivation of tomatoes rather than the controlled greenhouse environment. Although the scale of potential impacts is uncertain, there are techniques that can be implemented to predict these impacts. Global climate models (GCMs) are useful tools for the analysis of possible impacts on a species. The current study aims to determine the impacts of climate change and the major factors of abiotic stress that limit the open field cultivation of tomatoes in both the present and future, based on predicted global climate change using CLIMatic indEX and the A2 emissions scenario, together with the GCM Commonwealth Scientific and Industrial Research Organisation (CSIRO)-Mk3·0 (CS), for the years 2050 and 2100. The results indicate that large areas that currently have an optimum climate will become climatically marginal or unsuitable for open field cultivation of tomatoes due to progressively increasing heat and dry stress in the future. Conversely, large areas now marginal and unsuitable for open field cultivation of tomatoes will become suitable or optimal due to a decrease in cold stress. The current model may be useful for plant geneticists and horticulturalists who could develop new regional stress-resilient tomato cultivars based on needs related to these modelling projections.

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Dust Aerosol Important for Snowball Earth Deglaciation

Journal of Climate ◽

10.1175/2010jcli3378.1 ◽

2010 ◽

Vol 23 (15) ◽

pp. 4121-4132 ◽

Cited By ~ 26

Author(s):

Dorian S. Abbot ◽

Itay Halevy

Keyword(s):

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Atmospheric Model ◽

Dust Aerosol ◽

Snowball Earth ◽

Dust Aerosols ◽

Co2 Levels ◽

The Impact

Abstract Most previous global climate model simulations could only produce the termination of Snowball Earth episodes at CO2 partial pressures of several tenths of a bar, which is roughly an order of magnitude higher than recent estimates of CO2 levels during and shortly after Snowball events. These simulations have neglected the impact of dust aerosols on radiative transfer, which is an assumption of potentially grave importance. In this paper it is argued, using the Dust Entrainment and Deposition (DEAD) box model driven by GCM results, that atmospheric dust aerosol concentrations may have been one to two orders of magnitude higher during a Snowball Earth event than today. It is furthermore asserted on the basis of calculations using NCAR’s Single Column Atmospheric Model (SCAM)—a radiative–convective model with sophisticated aerosol, cloud, and radiative parameterizations—that when the surface albedo is high, such increases in dust aerosol loading can produce several times more surface warming than an increase in the partial pressure of CO2 from 10−4 to 10−1 bar. Therefore the conclusion is reached that including dust aerosols in simulations may reconcile the CO2 levels required for Snowball termination in climate models with observations.

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Temperature decrease through the Eocene-Oligocene transition controls eco-hydrologic shifts in Central Asia

10.5194/egusphere-egu2020-10034 ◽

2020 ◽

Author(s):

Alexander Rohrmann ◽

Guillaume Dupont-Nivet ◽

Michael Hren ◽

Dirk Sachse ◽

Niels Meijer ◽

...

Keyword(s):

Central Asia ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Temperature Drop ◽

Precipitation Amount ◽

Central China ◽

Broad Leaf ◽

Xining Basin ◽

The Impact

At ca. 34 Ma the Eocene-Oligocene transition (EOT) marks the shift from greenhouse conditions during the Eocene to the icehouse of the Oligocene and was the most pronounced cooling event during the Cenozoic. This event is well documented in marine records with a significant increase in benthic foraminifera &#948;18O values suggesting a 5&#176;C cooling in air temperature through the EOT. Instead, the few but growing number of terrestrial records suggest a much larger cooling of 4-9&#176;C. Yet, details regarding the exact timing of cooling and ensuing terrestrial changes in climate, hydrology, and ecology are sparse. Here, we investigate the impact of the EOT cooling event and associated climatic changes on the hydrology and vegetation in central China. We use stable isotopes of hydrogen (&#948;Dwax) and carbon (&#948;13Cwax) from leaf-waxes, a paleo-hydrology proxy obtained from organic material in sedimentary rocks, in combination with pollen data from a continuous well-dated, high-resolution sedimentary section from the Xining Basin in NE Tibet (36&#176;42' N, 101&#176;43' E). We then compare our results to a fully-coupled, global climate model (GCM) simulating the pre- and post-EOT conditions in central Asia.The obtained &#948;Dwax record ranges between -160 to -190&#8240; and shows a complex two-step transition through the EOT with a rapid initial drop of -30&#8240; from 33.9 to 33.7 Ma, a recovery to pre-EOT values between 33.7 to 33.4 Ma and a second drop similar in magnitude as the first one. In contrast, &#948;13Cwax values remain unchanged at -29 to -28&#8240; through the EOT. The GCM indicates a difference in temperature throughout the year between pre- and post-EOT runs of 8-9&#176;C at the Xining Basin with change in seasonality due to the collapse of the pre-EOT wet spring season, yielding mainly autumn precipitation after the transition. The overall precipitation amount remained in both simulations dry with < 500 mm/yr. The combined results show that the region experienced: (a) a significant temperature drop of 8-9&#176;C through the EOT being the first-order control on the records decrease in &#948;Dwax&#160; (1-2 &#8240; per 1&#176;C in mid-latitudes and up-to 5 &#8240; per 1&#176;C in higher latitudes) through the EOT; (b) constant bioproductivity and/or similar water-use efficiency within plants displayed by unchanged &#948;13Cwax values; (c) a changeover from a &#8220;warm-wet&#8221; desert abundant in Nitraria and Ephedra shrubs to a &#8220;temperate&#8221; desert with an expansion of conifers and broad-leaf trees in the higher-elevation hinterlands. We interpret that this change in seasonality and cooler EOT temperatures reduced the plant&#8217;s overall transpirational pressure, contributing to the spread of conifers and broad-leaf trees after the EOT under regionally new hydrologic conditions.

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Constraining the model representation of the aerosol life cycle in relation to sources and sinks.

10.5194/egusphere-egu2020-21948 ◽

2020 ◽

Author(s):

Paul Kim ◽

Daniel Partridge ◽

James Haywood

Keyword(s):

Climate Change ◽

Life Cycle ◽

Large Scale ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Evaluation Framework ◽

Microphysical Properties ◽

The Impact ◽

Source And Sink

Global climate model (GCM) ensembles still produce a significant spread of estimates for the future of climate change which hinders our ability to influence policymakers. The range of these estimates can only partly be explained by structural differences and varying choice of parameterisation schemes between GCMs. GCM representation of cloud and aerosol processes, more specifically aerosol microphysical properties, remain a key source of uncertainty contributing to the wide spread of climate change estimates. The radiative effect of aerosol is directly linked to the microphysical properties and these are in turn controlled by aerosol source and sink processes during transport as well as meteorological conditions.A Lagrangian, trajectory-based GCM evaluation framework, using spatially and temporally collocated aerosol diagnostics, has been applied to over a dozen GCMs via the AeroCom initiative. This framework is designed to isolate the source and sink processes that occur during the aerosol life cycle in order to improve the understanding of the impact of these processes on the simulated aerosol burden. Measurement station observations linked to reanalysis trajectories are then used to evaluate each GCM with respect to a quasi-observational standard to assess GCM skill. The AeroCom trajectory experiment specifies strict guidelines for modelling groups; all simulations have wind fields nudged to ERA-Interim reanalysis and all simulations use emissions from the same inventories. This ensures that the discrepancies between GCM parameterisations are emphasised and differences due to large scale transport patterns, emissions and other external factors are minimised.Preliminary results from the AeroCom trajectory experiment will be presented and discussed, some of which are summarised now. A comparison of GCM aerosol particle number size distributions against observations made by measurement stations in different environments will be shown, highlighting the difficulties that GCMs have at reproducing observed aerosol concentrations across all size ranges in pristine environments. The impact of precipitation during transport on aerosol microphysical properties in each GCM will be shown and the implications this has on resulting aerosol forcing estimates will be discussed. Results demonstrating the trajectory collocation framework will highlight its ability to give more accurate estimates of the key aerosol sources in GCMs and the importance of these sources in influencing modelled aerosol-cloud effects. In summary, it will be shown that this analysis approach enables us to better understand the drivers behind inter-model and model-observation discrepancies.

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Assessing the global risk of climate change to re/insurers using catastrophe models and hazard maps

10.5194/egusphere-egu2020-5323 ◽

2020 ◽

Author(s):

Sarah Jones ◽

Emma Raven ◽

Jane Toothill

Keyword(s):

Climate Change ◽

Sea Level ◽

Flood Risk ◽

River Flow ◽

Climate Model ◽

Flood Hazard ◽

Insurance Industry ◽

Global Climate Model ◽

Hazard Maps ◽

The Impact

In 2018 worldwide natural catastrophe losses were estimated at around USD $155 billion, resulting in the fourth-highest insurance payout on sigma records, and in 2020 JBA Risk Management (JBA) estimate 2 billion people will be at risk to inland flooding. By 2100, under a 1.5&#176;C warming scenario, the cost of coastal flooding alone as a result of sea level rise could reach USD $10.2 trillion per year, assuming no further adaptation. It is therefore imperative to understand the impact climate change may have on global flood risk and insured losses in the future.The re/insurance industry has an important role to play in providing financial resilience in a changing climate. Although integrating climate science into financial business remains in its infancy, modelling companies like JBA are increasingly developing new data and services to help assess the potential impact of climate change on insurance exposure.We will discuss several approaches to incorporating climate change projections with flood risk data using examples from research collaborations and commercial projects. Our case studies will include: (1) building a national-scale climate change flood model through the application of projected changes in river flow, rainfall and sea level to the stochastic event set in the model, and (2) using Global Climate Model data to adjust hydrological inputs driving 2D hydraulic models to develop climate change flood hazard maps.These tools provide outputs to meet different needs, and results may sometimes invoke further questions. For example: how can an extreme climate scenario produce lower flood risk than a conservative one? Why may adjacent postcodes' flood risk differ? We will explore the challenges associated with interpreting these results and the potential implications for the re/insurance industry.

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