Climate-driven regime shifts in a mangrove–salt marsh ecotone over the past 250 years

Climate change is driving the tropicalization of temperate ecosystems by shifting the range edges of numerous species poleward. Over the past few decades, mangroves have rapidly displaced salt marshes near multiple poleward mangrove range limits, including in northeast Florida. It is uncertain whether such mangrove expansions are due to anthropogenic climate change or natural climate variability. We combined historical accounts from books, personal journals, scientific articles, logbooks, photographs, and maps with climate data to show that the current ecotone between mangroves and salt marshes in northeast Florida has shifted between mangrove and salt marsh dominance at least 6 times between the late 1700s and 2017 due to decadal-scale fluctuations in the frequency and intensity of extreme cold events. Model projections of daily minimum temperature from 2000 through 2100 indicate an increase in annual minimum temperature by 0.5 °C/decade. Thus, although recent mangrove range expansion should indeed be placed into a broader historical context of an oscillating system, climate projections suggest that the recent trend may represent a more permanent regime shift due to the effects of climate change.

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Selecting climate projections for services: the DRIAS-2020 dataset

10.5194/egusphere-egu21-9527 ◽

2021 ◽

Author(s):

Lola Corre ◽

Samuel Somot ◽

Jean-Michel Soubeyroux ◽

Sébastien Bernus ◽

Agathe Drouin ◽

...

Keyword(s):

Climate Change ◽

Regional Climate ◽

Easy Access ◽

Climate Projections ◽

Climate Scenarios ◽

Climate Data ◽

Quantile Mapping ◽

The Past ◽

Climate Service ◽

Climatic Information

The French National Climate Service &#8220;Drias, futures of climate&#8221; was launched in 2012, as a response of the French scientific community to society&#8217;s need for climatic information. It is mainly composed of a website that provides easy access to the best available climate data to characterize climate change over France. Latest advances developed in 2020 include the availability of a new set of regional climate scenarios corrected by a quantile-mapping based method with correction depending on the weather regime. As for the previous set, the climate projections are based on the EURO-CORDEX ensemble, whose contents have been greatly enriched over the past years. Singular effort was done to build a robust and synthetic set that well represents the uncertainties of climate change over France. The different criteria defined to select the simulations will be presented, and the range of the projected climate change will be examined, with respect to larger ensembles.&#160;

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Strong controls of daily minimum temperature on the autumn photosynthetic phenology of subtropical vegetation in China

Forest Ecosystems ◽

10.1186/s40663-021-00309-9 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Peixin Ren ◽

Zelin Liu ◽

Xiaolu Zhou ◽

Changhui Peng ◽

Jingfeng Xiao ◽

...

Keyword(s):

Climate Change ◽

Minimum Temperature ◽

Vegetation Index ◽

Climatic Factors ◽

Time Lag ◽

Evergreen Forest ◽

Maximum Temperature ◽

Daily Minimum Temperature ◽

Study Region ◽

Cumulative Precipitation

Abstract Background Vegetation phenology research has largely focused on temperate deciduous forests, thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions. Results Using satellite solar-induced chlorophyll fluorescence (SIF) and MODIS enhanced vegetation index (EVI) data, we applied two methods to evaluate temporal and spatial patterns of the end of the growing season (EGS) in subtropical vegetation in China, and analyze the dependence of EGS on preseason maximum and minimum temperatures as well as cumulative precipitation. Our results indicated that the averaged EGS derived from the SIF and EVI based on the two methods (dynamic threshold method and derivative method) was later than that derived from gross primary productivity (GPP) based on the eddy covariance technique, and the time-lag for EGSsif and EGSevi was approximately 2 weeks and 4 weeks, respectively. We found that EGS was positively correlated with preseason minimum temperature and cumulative precipitation (accounting for more than 73% and 62% of the study areas, respectively), but negatively correlated with preseason maximum temperature (accounting for more than 59% of the study areas). In addition, EGS was more sensitive to the changes in the preseason minimum temperature than to other climatic factors, and an increase in the preseason minimum temperature significantly delayed the EGS in evergreen forests, shrub and grassland. Conclusions Our results indicated that the SIF outperformed traditional vegetation indices in capturing the autumn photosynthetic phenology of evergreen forest in the subtropical region of China. We found that minimum temperature plays a significant role in determining autumn photosynthetic phenology in the study region. These findings contribute to improving our understanding of the response of the EGS to climate change in subtropical vegetation of China, and provide a new perspective for accurately evaluating the role played by evergreen vegetation in the regional carbon budget.

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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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Future humidity extremes in an urban-rural context - using regional climate model projections down to convection permitting scales for Berlin

10.5194/egusphere-egu21-12054 ◽

2021 ◽

Author(s):

Gaby S. Langendijk ◽

Diana Rechid ◽

Daniela Jacob

Keyword(s):

Climate Change ◽

Urban Areas ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Climate Change Impacts ◽

Regional Climate Models ◽

Climate Projections ◽

Climate Data ◽

Urban Rural

Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2&#176;C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.

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Australian Seaport Infrastructure Resilience to Climate Change

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.238.350 ◽

2012 ◽

Vol 238 ◽

pp. 350-357 ◽

Cited By ~ 1

Author(s):

Daniel Kong ◽

Sujeeva Setunge ◽

Thomas C.K. Molyneaux ◽

Guo Min Zhang ◽

David W. Law

Keyword(s):

Climate Change ◽

Current Knowledge ◽

Climate Projections ◽

Climate Data ◽

Budget Planning ◽

Capital Budget ◽

Changing Climate ◽

Port Authorities ◽

Port Structures ◽

Port Infrastructure

A research project continuing at RMIT University is exploring the resilience of port structures in a changing climate. Research completed to date comprises of identifying types of port infrastructure vulnerable to climate change, establishing materials and exposure conditions, developing deterioration models based on current knowledge to simulate the effect of climate change on key port infrastructure and modeling the selected elements of infrastructure to derive outcomes which will aid in decision making in port infrastructure management. A considerable effort has been concentrated on identifying input climate data most appropriate for the models developed. The modeling approach is presented in this paper for quantitative projections of damage probability on port infrastructure taking into account the variability of material type, design considerations and environmental exposures with a changing climate. This paper provides a summary of the research undertaken in the development of material deterioration models and their responses to a changing climate load. Using climate information drawn from historical weather records and future climate projections, existing deterioration models were refined to include climate data into modeling runs in order to analyse changes to deterioration rates of different materials when impacted by a change in climate variables. Outputs from this modeling process will assist port authorities in making informed decisions on maintenance and capital budget planning allowing for impacts of climate change.

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Indices for daily temperature and precipitation based on quality controlled and homogenized data in Madagascar

10.5194/egusphere-egu2020-19970 ◽

2020 ◽

Author(s):

Luc Yannick Andréas Randriamarolaza ◽

Enric Aguilar ◽

Oleg Skrynyk

Keyword(s):

Climate Change ◽

Missing Data ◽

Extreme Temperature ◽

Maximum Temperature ◽

Daily Minimum Temperature ◽

Daily Maximum Temperature ◽

Climate Indices ◽

Low Density ◽

Climate Data ◽

Temperature And Precipitation

Madagascar is an Island in Western Indian Ocean Region. It is mainly exposed to the easterly trade winds and has a rugged topography, which promote different local climates and biodiversity. Climate change inflicts a challenge on Madagascar socio-economic activities. However, Madagascar has low density station and sparse networks on observational weather stations to detect changes in climate. On average, one station covers more than 20 000 km2 and closer neighbor stations are less correlated. Previous studies have demonstrated the changes on Madagascar climate, but this paper contributes and enhances the approach to assess the quality control and homogeneity of Madagascar daily climate data before developing climate indices over 1950 &#8211; 2018 on 28 synoptic stations. Daily climate data of minimum and maximum temperature and precipitation are exploited.Firstly, the quality of daily climate data is controlled by INQC developed and maintained by Center for Climate Change (C3) of Rovira i Virgili University, Spain. It ascertains and improves error detections by using six flag categories. Most errors detected are due to digitalization and measurement.Secondly, daily quality controlled data are homogenized by using CLIMATOL. It uses relative homogenization methods, chooses candidate reference series automatically and infills the missing data in the original data. It has ability to manage low density stations and low inter-station correlations and is tolerable for missing data. Monthly break points are detected by CLIMATOL and used to split daily climate data to be homogenized.Finally, climate indices are calculated by using CLIMIND package which is developed by INDECIS* project. Compared to previous works done, data period is updated to 10 years before and after and 15 new climate indices mostly related to extremes are computed. On temperature, significant increasing and decreasing decade trends of day-to-day and extreme temperature ranges are important in western and eastern areas respectively. On average decade trends of temperature extremes, significant increasing of daily minimum temperature is greater than daily maximum temperature. Many stations indicate significant decreasing in very cold nights than significant increasing in very warm days. Their trends are almost 1 day per decade over 1950 &#8211; 2018. Warming is mainly felt during nighttime and daytime in Oriental and Occidental parts respectively. In contrast, central uplands are warming all the time but tropical nights do not appear yet. On rainfall, no major significant findings are found but intense precipitation might be possible at central uplands due to shortening of longest wet period and occurrence of heavy precipitation. However, no influence detected on total precipitation which is still decreasing over 1950 - 2018. Future works focus on merging of relative homogenization methodologies to ameliorate the results.-------------------*INDECIS is a part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462).

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Comparing precipitation and temperature trends between inland and coastal locations

ANZIAM Journal ◽

10.21914/anziamj.v60i0.13967 ◽

2019 ◽

Vol 60 ◽

pp. C109-C126 ◽

Cited By ~ 1

Author(s):

Joshua Hartigan ◽

Shev MacNamara ◽

Lance M Leslie

Keyword(s):

Climate Change ◽

Minimum Temperature ◽

Southern Oscillation ◽

Power Spectra ◽

Maximum Temperature ◽

P Value ◽

Wet Season ◽

Intergovernmental Panel ◽

The Past ◽

The Mean

Motivated by the Millennium Drought and the current drought over much of southern and eastern Australia, this detailed statistical study compares trends in annual wet season precipitation and temperature between a coastal site (Newcastle) and an inland site (Scone). Bootstrap permutation tests reveal Scone precipitation has decreased significantly over the past 40 years (p-value=0.070) whereas Newcastle has recorded little to no change (p-value=0.800). Mean maximum and minimum temperatures for Newcastle have increased over the past 40 years (p-values of 0.002 and 0.015, respectively) while the mean maximum temperature for Scone has increased (p-value = 0.058) and the mean minimum temperature has remained stable. This suggests mean temperatures during the wet season for both locations are increasing. Considering these trends along with those for precipitation, water resources in the Hunter region will be increasingly strained as a result of increased evaporation with either similar or less precipitation falling in the region. Wavelet analysis reveals that both sites have similar power spectra for precipitation and mean maximum temperature with a statistically significant signal in the two to seven year period, typically indicative of the El-Nino Southern Oscillation climate driver. The El-Nino Southern Oscillation also drives the Newcastle mean minimum temperature, whereas the Scone power spectra has no indication of a definitive driver for mean minimum temperature. References R. A., R. L. Kitching, F. Chiew, L. Hughes, P. C. D. Newton, S. S. Schuster, A. Tait, and P. Whetton. Climate change 2014: Impacts, adaptation, and vulnerability. Part B: Regional aspects. Contribution of Working Group II to the Fifth Assessment of the Intergovernmental Panel on Climate Change. Technical report, Intergovernmental Panel on Climate Change, 2014. URL https://www.ipcc.ch/report/ar5/wg2/. Bureau of Meteorology. Climate Glossary-Drought. URL http://www.bom.gov.au/climate/glossary/drought.shtml. K. M. Lau and H. Weng. Climate signal detection using wavelet transform: How to make a time series sing. B. Am. Meteorol. Soc., 76:23912402, 1995. doi:10.1175/1520-0477(1995)0762391:CSDUWT>2.0.CO;2. M. B. Richman and L. M. Leslie. Uniqueness and causes of the California drought. Procedia Comput. Sci., 61:428435, 2015. doi:10.1016/j.procs.2015.09.181. M. B. Richman and L. M. Leslie. The 20152017 Cape Town drought: Attribution and prediction using machine learning. Procedia Comput. Sci., 140:248257, 2018. doi:10.1016/j.procs.2018.10.323.

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Dynamics of Vegetation Greenness and Its Response to Climate Change in Xinjiang over the Past Two Decades

Remote Sensing ◽

10.3390/rs13204063 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4063

Author(s):

Jie Xue ◽

Yanyu Wang ◽

Hongfen Teng ◽

Nan Wang ◽

Danlu Li ◽

...

Keyword(s):

Climate Change ◽

Vegetation Change ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Dynamic Change ◽

Google Earth ◽

Arid Areas ◽

Climate Data ◽

Northern Margin ◽

The Past

Climate change has proven to have a profound impact on the growth of vegetation from various points of view. Understanding how vegetation changes and its response to climatic shift is of vital importance for describing their mutual relationships and projecting future land–climate interactions. Arid areas are considered to be regions that respond most strongly to climate change. Xinjiang, as a typical dryland in China, has received great attention lately for its unique ecological environment. However, comprehensive studies examining vegetation change and its driving factors across Xinjiang are rare. Here, we used the remote sensing datasets (MOD13A2 and TerraClimate) and data of meteorological stations to investigate the trends in the dynamic change in the Normalized Difference Vegetation Index (NDVI) and its response to climate change from 2000 to 2019 across Xinjiang based on the Google Earth platform. We found that the increment rates of growth-season mean and maximum NDVI were 0.0011 per year and 0.0013 per year, respectively, by averaging all of the pixels from the region. The results also showed that, compared with other land use types, cropland had the fastest greening rate, which was mainly distributed among the northern Tianshan Mountains and Southern Junggar Basin and the northern margin of the Tarim Basin. The vegetation browning areas primarily spread over the Ili River Valley where most grasslands were distributed. Moreover, there was a trend of warming and wetting across Xinjiang over the past 20 years; this was determined by analyzing the climate data. Through correlation analysis, we found that the contribution of precipitation to NDVI (R2 = 0.48) was greater than that of temperature to NDVI (R2 = 0.42) throughout Xinjiang. The Standardized Precipitation and Evapotranspiration Index (SPEI) was also computed to better investigate the correlation between climate change and vegetation growth in arid areas. Our results could improve the local management of dryland ecosystems and provide insights into the complex interaction between vegetation and climate change.

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The rate of ecological acclimation is the dominant uncertainty in long-term projections of a key ecosystem service

10.1101/2021.08.11.455579 ◽

2021 ◽

Author(s):

Andrew J. Felton ◽

Robert K. Shriver ◽

Michael Stemkovski ◽

John B. Bradford ◽

Katharine N. Suding ◽

...

Keyword(s):

Climate Change ◽

Ecosystem Services ◽

Greenhouse Gas Emission ◽

Species Turnover ◽

Spatial Gradient ◽

Climate Data ◽

Forage Production ◽

Global Circulation Models ◽

Decadal Scale

The potential for ecosystems to continue providing society with essential services may depend on their ability to acclimate to climate change through multiple processes operating from cells to landscapes. While models to predict climate change impacts on ecosystem services often consider uncertainty among greenhouse gas emission scenarios or global circulation models (GCMs), they rarely consider the rate of ecological acclimation, which depends on decadal-scale processes such as species turnover. Here we show that uncertainty due to the unknown rate of ecological acclimation is larger than other sources of uncertainty in late-century projections of forage production in US rangelands. Combining statistical models fit to historical climate data and remotely-sensed estimates of herbaceous productivity with an ensemble of GCMs, we projected changes in forage production using two approaches. The time-series approach, which assumes minimal acclimation, projects widespread decreases in forage production. The spatial gradient approach, which assumes ecological acclimation keeps pace with climate, predicts widespread increases in forage production. This first attempt to quantify the magnitude of a critical uncertainty emphasizes that better understanding of ecological acclimation is essential to improve long-term forecasts of ecosystem services, and shows that management to facilitate ecological acclimation may be necessary to maintain ecosystem services at historical baselines.

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Climate Change Explorer: Extracting localized data for developing Climate Services

10.5194/egusphere-egu21-12336 ◽

2021 ◽

Author(s):

Christine Nam ◽

Bente Tiedje ◽

Susanne Pfeifer ◽

Diana Rechid ◽

Daniel Eggert

Keyword(s):

Climate Change ◽

Regional Climate Model ◽

Climate Model ◽

Regional Climate ◽

Heavy Precipitation ◽

Climate Projections ◽

Climate Data ◽

Climate Services ◽

Mitigation And Adaptation ◽

Data Formats

Everyone, politicians, public administrations, business owners, and citizens want to know how climate changes will affect them locally. Having such knowledge offers everyone the opportunity to make informed choices and take action towards mitigation and adaptation.&#160;In order to develop locally relevant climate service products and climate advisory services, as we do at GERICS, we must extract localized climate change information from Regional Climate Model ensemble simulations.&#160;Common challenges associated with developing such services include the transformation of petabytes of data from physical quantities such as precipitation, temperature, or wind, into user-applicable quantities such as return periods of heavy precipitation, e.g. for legislative or construction design frequency. Other challenges include the technical and physical barriers in the use and interpretation of climate data, due to large data volume, unfamiliar software and data formats, or limited technical infrastructure. The interpretation of climate data also requires scientific background knowledge, which limit or influence the interpretation of results.&#160;These barriers hinder the efficient and effective transformation of big data into user relevant information in a timely and reliable manner. To enable our society to adapt and become more resilient to climate change, we must overcome these barriers. In the Helmholtz funded Digital Earth project we are tackling these challenges by developing a Climate Change Workflow.&#160;In the scope of this Workflow, the user can easily define a region of interest and extract the relevant climate data from the simulations available at the Earth System Grid Federation (ESGF). Following which, a general overview of the projected changes, in precipitation for example, for multiple climate projections is presented. It conveys the bandwidth, i.e. the minimum/maximum range by an ensemble of regional climate model projections. We implemented the sketched workflow in a web-based tool called The Climate Change Explorer. It addresses barriers associated with extracting locally relevant climate data from petabytes of data, in unfamilar data formats, and deals with interpolation issues, using a more intuitive and user-friendly web interface.&#160;Ultimately, the Climate Change Explorer provides concise information on the magnitude of projected climate change and the range of these changes for individually defined regions, such as found in GERICS &#8216;Climate Fact Sheets&#8217;. This tool has the capacity to also improve other workflows of climate services, allowing them to dedicate more time in deriving user relevant climate indicies; enabling politicians, public administrations, and businesses to take action.

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