Climate change impact on the distribution of Tossa jute using maximum entropy and educational global climate modelling

Tossa (Corchorus olitorius L.) is a significant cash crop, cultivated commercially in the lower flood plain of Bangladesh. The climatic regimes in Bangladesh are changing as well as the world does. However, this species is threatened by climate change. Occurrences of data on threatened and endangered species are frequently sparse which makes it difficult to analyse the species suitable habitat distribution using various modelling approaches. The current paper used maximum entropy (Maxent) and educational global climate model (EdGCM) modelling to predict and conserve the suitable habitat distributions for Tossa species in Bangladesh to the year 2100. Nine environmental variables, 239 occurrence data and two Representative Concentration Pathway scenarios (RCP4.5 and RCP8.5) were used for the Maxent modelling to project the impact of climate change on the Tossa distributions. Furthermore, the EdGCM was used to study the climatic space suitability for the Tossa species in the context of Bangladesh. Both of the climatic scenarios were used for the prediction to the year 2100. The Maxent model performed better than random for the Tossa species with a high AUC value of 0.86. Under the RCP scenarios, the Maxent model predicted habitat reduction for RCP4.5 is 2%, RCP8.5 is 9% and EdGCM is 10.2% from the current localities. The predictive modelling approach presented here is promising and can be applied to other important species for conservation planning, monitoring and management, especially those under the threat of extinction due to climate change.

Estimating unprecedented extremes in UK summer daily rainfall

The UNSEEN (UNprecedented Simulated Extremes using ENsembles) method involves using a large ensemble of climate model simulations to increase the sample size of rare events. Here we extend UNSEEN to focus on intense summertime daily rainfall, estimating plausible rainfall extremes in the current climate. To address modelling limitations simulations from two climate models were used; an initialised 25km global model that uses parametrized convection, and a dynamically downscaled 2.2km model that uses explicit convection. In terms of the statistical characteristics that govern very rare return periods, the models are not significantly different from the observations across much of the UK. Our analysis provides more precise estimates of 1000-year return levels for extreme daily rainfall, reducing sampling uncertainty by 70-90% compared to using observations alone. This framework enables observed daily storm profiles to be adjusted to more statistically robust estimates of extreme rainfall. For a damaging storm in July 2007 which led to surface water flooding, we estimate physically plausible increases in the total daily rainfall of 50 – 100%. For much of the UK the annual chance of record-breaking daily summertime rainfall is estimated to be around 1% per year in the present-day climate. Analysis of the dynamical states in our UNSEEN events indicates that heavy daily rainfall is associated with a southward displaced and meandering North Atlantic jet stream, increasing the advection of warm moist air from across Southern Europe and the Mediterranean, and intensifying extratropical storms. This work represents an advancement in the use of climate modelling for estimating present-day climate hazards and outlines a framework for applying UNSEEN at higher spatial and temporal resolutions.

Characterisation of the Antarctic stratospheric vortex mixing barrier

<p>The strongest stratospheric circulation in the Southern Hemisphere is the Antarctic Circumpolar Vortex (ACV) which forms each winter and spring as a zone of westerly winds surrounding Antarctica, presenting a barrier to transport of air masses between middle and high-latitudes. This barrier contributes to stratospheric temperatures above the polar region dropping sufficiently low in spring to allow for the processes leading to ozone destruction. Unfortunately, the ACV is generally not well simulated in Global Climate Models (GCMs), and this presents a challenge for model accuracy and projections in the face of a changing climate and a recovering ozone hole. In this research, an assessment is made of the performance of a range of mixing metrics in representing the ACV based on reanalyses, including: Effective Diffusivity, Contour Crossing, the Lagrangian function $M$, and Meridional Impermeability. It is shown that Meridional Impermeability -- which provides a measure of the strength of the meridional mixing barrier as a function of potential vorticity (PV) gradient and wind-speed -- acts as a useful proxy for more complex metrics. In addition, Meridional Impermeability displays a well-defined vortex profile across equivalent latitude, which is not seen to the same degree in the other metrics assessed. Representation of the ACV is further compared between climate models and reanalyses based on Meridional Impermeability. It is shown that while climate models have improved in their representation of the vortex barrier over time, there are still significant discrepancies between models and reanalyses. One cause of these discrepancies may result from the use of prescribed ozone fields rather than interactive ozone chemistry. This is further examined by comparing Chemistry Climate Model (CCM) simulations using interactive ozone chemistry, with those using prescribed ozone at either 3-D (i.e., height, latitude and longitude) or 2-D (i.e., height, latitude) dimensionality. Considerable improvement in the representation of the ACV can be achieved by shifting from 2-D to 3-D prescribed ozone fields, and interactive ozone chemistry further improves its representation. However, discrepancies in model representation of the ACV still remain. Previous researchers have also attributed discrepancies in model representation of the polar vortices to the model resolution, and the parameterization of gravity waves at the sub-grid scale -- these factors are considered to contribute to the discrepancies found in simulations undertaken here also. The results of this research are expected to provide guidance to improve the representation of vortex processes in climate modelling.</p>

Characterisation of the Antarctic stratospheric vortex mixing barrier

<p>The strongest stratospheric circulation in the Southern Hemisphere is the Antarctic Circumpolar Vortex (ACV) which forms each winter and spring as a zone of westerly winds surrounding Antarctica, presenting a barrier to transport of air masses between middle and high-latitudes. This barrier contributes to stratospheric temperatures above the polar region dropping sufficiently low in spring to allow for the processes leading to ozone destruction. Unfortunately, the ACV is generally not well simulated in Global Climate Models (GCMs), and this presents a challenge for model accuracy and projections in the face of a changing climate and a recovering ozone hole. In this research, an assessment is made of the performance of a range of mixing metrics in representing the ACV based on reanalyses, including: Effective Diffusivity, Contour Crossing, the Lagrangian function $M$, and Meridional Impermeability. It is shown that Meridional Impermeability -- which provides a measure of the strength of the meridional mixing barrier as a function of potential vorticity (PV) gradient and wind-speed -- acts as a useful proxy for more complex metrics. In addition, Meridional Impermeability displays a well-defined vortex profile across equivalent latitude, which is not seen to the same degree in the other metrics assessed. Representation of the ACV is further compared between climate models and reanalyses based on Meridional Impermeability. It is shown that while climate models have improved in their representation of the vortex barrier over time, there are still significant discrepancies between models and reanalyses. One cause of these discrepancies may result from the use of prescribed ozone fields rather than interactive ozone chemistry. This is further examined by comparing Chemistry Climate Model (CCM) simulations using interactive ozone chemistry, with those using prescribed ozone at either 3-D (i.e., height, latitude and longitude) or 2-D (i.e., height, latitude) dimensionality. Considerable improvement in the representation of the ACV can be achieved by shifting from 2-D to 3-D prescribed ozone fields, and interactive ozone chemistry further improves its representation. However, discrepancies in model representation of the ACV still remain. Previous researchers have also attributed discrepancies in model representation of the polar vortices to the model resolution, and the parameterization of gravity waves at the sub-grid scale -- these factors are considered to contribute to the discrepancies found in simulations undertaken here also. The results of this research are expected to provide guidance to improve the representation of vortex processes in climate modelling.</p>

The Earth's climate lagged, recurrent and non-linear solar and lunar multi-millennial scale responses: An oceanic hypothesis, evidence, verifications and forecasts

This work provides a hypothesis of the links between the multi-millennia scale recurrent solar and tidal influences and Earth's climate lagged responses, associated with the oceanic transport mechanisms with a variable modulation. As a part of this hypothesis, empirical and simple, non-linear lagged models are proposed for five of the most representative Earth's climate variables (a continental tropical temperature, an Antarctic temperature [at James Ross Island], the Greenland temperature, the global temperature and the southeast asian monsoon) with multi-millennia records to account for the lagged responses to solar forcing. The proposed models implicitely include a well-known oceanic heat transport mechanism: the Ocean Conveyor Belt. This oceanic mechanism appears to generate a climate modulation through the intensity of the ocean/atmosphere circulation, and a heat and mass transport, with a consequent climate lag of several thousands of years. Tidal forcing is also considered for global temperature modelling and forecast. The consequent millennia-scale global forecasts, after being integrated/verified with an accumulated ocean travelled distance from the tropical East Pacific, and with a double evaluation of the tidal influences based on similarities and on the NASA’s solar system astronomical dynamics, indicates a cooling for the next century, and gentle oscillations over the next millennia. Our preliminary results that strongly suggest that millennial scale changes in solar activity induce circulation and thermal global impacts, also suggest that the Younger Dryas event, may be influenced by the lagged outcomes of solar driven changes in the tropical Pacific, and by tidal influences. The detected Earth's climate delayed responses, that have been working in the past and present climates, and will be working in the future climates, must be, as soon as possible, independently verified and theoretically sustained, before to be fully included in a multi-scale climate models as a scientific theory. A final example for the global temperature record over the last 170 years demonstrates with experimental results for the twenty first century evolution the convenience of a multi-scale climate modelling with contrasting lower values compared with the IPCC global temperature scenarios.

Sentinel2GlobalLULC: A deep-learning-ready Sentinel-2 RGB image dataset for global land use/cover mapping

ABSTRACTLand-Use and Land-Cover (LULC) mapping is relevant for many applications, from Earth system and climate modelling to territorial and urban planning. Global LULC products are continuously developing as remote sensing data and methods grow. However, there is still low consistency among LULC products due to low accuracy for some regions and LULC types. Here, we introduce Sentinel2GlobalLULC, a Sentinel-2 RGB image dataset, built from the consensus of 15 global LULC maps available in Google Earth Engine. Sentinel2GlobalLULC v1.1 contains 195572 RGB images organized into 29 global LULC mapping classes. Each image is a tile that has 224 × 224 pixels at 10 × 10 m spatial resolution and was built as a cloud-free composite from all Sentinel-2 images acquired between June 2015 and October 2020. Metadata includes a unique LULC type annotation per image, together with level of consensus, reverse geo-referencing, and global human modification index. Sentinel2GlobalLULC is optimized for the state-of-the-art Deep Learning models to provide a new gate towards building precise and robust global or regional LULC maps.

Enhanced flood hazard assessment beyond decadal climate cycles based on centennial historical data (Duero basin, Spain)

Current climate modelling frameworks present significant uncertainties when it comes to quantifying flood quantiles in the context of climate change, calling for new information and strategies in hazard assessments. Here, state-of-the-art methods on hydraulic and statistical modelling are applied to historical and contemporaneous flood records to evaluate flood hazards beyond natural climate cycles. A comprehensive flood record of the Duero River in Zamora (Spain) was compiled from documentary sources, early water-level readings and continuous gauge records spanning the last 500 years. Documentary evidence of flood events includes minute books (municipal and ecclesiastic), narrative descriptions, epigraphic marks, newspapers and technical reports. We identified 69 flood events over the period 1250 to 1871, of which 15 were classified as catastrophic floods, 16 as extraordinary floods and 38 as ordinary floods. Subsequently, a two-dimensional hydraulic model was implemented to relate flood stages (flood marks and inundated areas) to discharges. The historical flood records show the largest floods over the last 500 years occurred in 1860 (3450 m3 s−1), 1597 (3200 m3 s−1) and 1739 (2700 m3 s−1). Moreover, at least 24 floods exceeded the perception threshold of 1900 m3 s−1 during the period (1500–1871). Annual maximum flood records were completed with gauged water-level readings (pre-instrumental dataset, PRE: 1872–1919) and systematic gauge records (systematic dataset, SYS: 1920–2018). The flood frequency analyses were based on (1) the expected moments algorithm (EMA) and (2) the maximum likelihood estimator (MLE) method, using five datasets with different temporal frameworks (historic dataset, HISTO: 1511–2018; PRE–SYS: 1872–2018; full systematic record, ALLSYS: 1920–2018; SYS1: 1920–1969; and SYS2: 1970–2018). The most consistent results were obtained using the HISTO dataset, even for high quantiles (0.001 % annual exceedance probability, AEP). PRE–SYS was robust for the 1 % AEP flood with increasing uncertainty in the 0.2 % AEP or 500-year flood, and ALLSYS results were uncertain in the 1 % and 0.2 % AEP floods. Since the 1970s, the frequency of extraordinary floods (>1900 m3 s−1) declined, although floods on the range of the historical perception threshold occurred in 2001 (2075 m3 s−1) and 2013 (1654 m3 s−1). Even if the future remains uncertain, this bottom-up approach addresses flood hazards under climate variability, providing real and certain flood discharges. Our results can provide a guide on low-regret adaptation decisions and improve public perception of extreme flooding.

Decadal climate predictions with the Canadian Earth System Model version 5 (CanESM5)

The Canadian Earth System Model version 5 (CanESM5) developed at Environment and Climate Change Canada's Canadian Centre for Climate Modelling and Analysis (CCCma) is participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). A 40-member ensemble of CanESM5 retrospective decadal forecasts (or hindcasts) is integrated for 10 years from realistic initial states once a year during 1961 to the present using prescribed external forcing. The results are part of CCCma's contribution to the Decadal Climate Prediction Project (DCPP) component of CMIP6. This paper evaluates CanESM5 large ensemble decadal hindcasts against observational benchmarks and against historical climate simulations initialized from pre-industrial control run states. The focus is on the evaluation of the potential predictability and actual skill of annual and multi-year averages of key oceanic and atmospheric fields at regional and global scales. The impact of initialization on prediction skill is quantified from the hindcasts decomposition into uninitialized and initialized components. The dependence of potential and actual skill on ensemble size is examined. CanESM5 decadal hindcasts skillfully predict upper-ocean states and surface climate with a significant impact from initialization that depend on climate variable, forecast range, and geographic location. Deficiencies in the skill of North Atlantic surface climate are identified and potential causes discussed. The inclusion of biogeochemical modules in CanESM5 enables the prediction of carbon cycle variables which are shown to be potentially skillful on decadal timescales, with a strong long-lasting impact from initialization on skill in the ocean and a moderate short-lived impact on land.