Backcasting long-term climate data: evaluation of hypothesis

2017 ◽  
Vol 132 (3-4) ◽  
pp. 717-726 ◽  
Author(s):  
Bahram Saghafian ◽  
Sara Ghasemi Aghbalaghi ◽  
Mohsen Nasseri
Keyword(s):  
Data Evaluation ◽  
Climate Data

scholarly journals Climate drivers of plague epidemiology in British India, 1898–1949

2020 ◽  
Vol 287 (1928) ◽  
pp. 20200538
Author(s):  
Warren S. D. Tennant ◽  
Mike J. Tildesley ◽  
Simon E. F. Spencer ◽  
Matt J. Keeling
Keyword(s):  
Predictive Models ◽  
Control Measures ◽  
Climate Data ◽  
British India ◽  
Middle Income ◽  
Middle Income Countries ◽  
Complex Interactions ◽  
The World ◽  
Spatio Temporal

Plague, caused by Yersinia pestis infection, continues to threaten low- and middle-income countries throughout the world. The complex interactions between rodents and fleas with their respective environments challenge our understanding of human plague epidemiology. Historical long-term datasets of reported plague cases offer a unique opportunity to elucidate the effects of climate on plague outbreaks in detail. Here, we analyse monthly plague deaths and climate data from 25 provinces in British India from 1898 to 1949 to generate insights into the influence of temperature, rainfall and humidity on the occurrence, severity and timing of plague outbreaks. We find that moderate relative humidity levels of between 60% and 80% were strongly associated with outbreaks. Using wavelet analysis, we determine that the nationwide spread of plague was driven by changes in humidity, where, on average, a one-month delay in the onset of rising humidity translated into a one-month delay in the timing of plague outbreaks. This work can inform modern spatio-temporal predictive models for the disease and aid in the development of early-warning strategies for the deployment of prophylactic treatments and other control measures.

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

scholarly journals The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

2021 ◽  
Vol 13 (9) ◽  
pp. 1701
Author(s):  
Leonardo Bagaglini ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
Giulia Panegrossi
Keyword(s):  
Neural Network ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
High Quality ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

scholarly journals A method for simulating risk profiles of wheat yield in data-sparse conditions

2021 ◽  
pp. 1-12
Author(s):  
G. Bracho-Mujica ◽  
P.T. Hayman ◽  
V.O. Sadras ◽  
B. Ostendorf
Keyword(s):  
Reference Site ◽  
Climate Impacts ◽  
Weather Data ◽  
Climate Data ◽  
High Quality ◽  
Risk Profiles ◽  
Record Length ◽  
Daily Weather Data ◽  
Study Sites

Abstract Process-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

Nondestructive Sampling to Monitor Macrophomina phaseolina Root Colonization in Overwintering Stevia

2021 ◽  
Author(s):  
Layne W. Rogers ◽  
Alyssa M. Koehler
Keyword(s):  
Root Colonization ◽  
Root Zone ◽  
Macrophomina Phaseolina ◽  
Field Trials ◽  
Soil Core ◽  
Climate Data ◽  
Sample Weight ◽  
The Family ◽  
Soil Temperatures

Macrophomina phaseolina is a soilborne fungal pathogen in the family Botryosphaeriaceae. Microsclerotia of M. phaseolina were first observed at the base of overwintering stevia stems in North Carolina in spring 2016. Previous studies utilized destructive sampling methods to monitor M. phaseolina in stevia fields; however, these methods are not feasible for long-term monitoring of disease in a perennial system. In the current study, nondestructive root soil-core sampling was conducted during overwintering months, from October 2018 to January 2020, to monitor M. phaseolina root colonization in stevia in Rocky Mount, NC. Two-inch-diameter soil cores were collected through the root zone, and fresh weight of roots was recorded for each soil core. M. phaseolina recovery was evaluated by examining mycelial growth from roots plated onto potato dextrose agar. There was no significant effect of sample weight on M. phaseolina across all dates, but there was one date for which sample weight had a significant effect on recovery (P = 0.01; α = 0.05). For both recovery and sample weight, sampling date was a significant predictor (P = 1.68e-5 and P = 0.0389, respectively; α = 0.05). Weather and climate data revealed that dates with no M. phaseolina recovery had lowest mean air and soil temperatures and the greatest number of days below freezing in the month prior to sampling. In separate sampling years, October sampling dates had the highest recovery of M. phaseolina. Future field trials should determine if October samplings can predict survival and vigor of reemerging stevia plants.

scholarly journals The Impact of Climate Change on the Water Balance of Oil Sands Reclamation Covers and Natural Soil Profiles

2018 ◽  
Vol 19 (11) ◽  
pp. 1731-1752 ◽  
Author(s):  
Md. Shahabul Alam ◽  
S. Lee Barbour ◽  
Amin Elshorbagy ◽  
Mingbin Huang
Keyword(s):  
Oil Sands ◽  
Weather Generator ◽  
First Century ◽  
Soil Profiles ◽  
Percentage Increase ◽  
Climate Data ◽  
Historical Climate ◽  
Twenty First Century ◽  
The Impact

Abstract The design of reclamation soil covers at oil sands mines in northern Alberta, Canada, has been conventionally based on the calibration of soil–vegetation–atmosphere transfer (SVAT) models against field monitoring observations collected over several years, followed by simulations of long-term performance using historical climate data. This paper evaluates the long-term water balances for reclamation covers on two oil sands landforms and three natural coarse-textured forest soil profiles using both historical climate data and future climate projections. Twenty-first century daily precipitation and temperature data from CanESM2 were downscaled based on three representative concentration pathways (RCPs) employing a stochastic weather generator [Long Ashton Research Station Weather Generator (LARS-WG)]. Relative humidity, wind speed, and net radiation were downscaled using the delta change method. Downscaled precipitation and estimated potential evapotranspiration were used as inputs to simulate soil water dynamics using physically based models. Probability distributions of growing season (April–October) actual evapotranspiration (AET) and net percolation (NP) for the baseline and future periods show that AET and NP at all sites are expected to increase throughout the twenty-first century regardless of RCP, time period, and soil profile. Greater increases in AET and NP are projected toward the end of the twenty-first century. The increases in future NP at the two reclamation covers are larger (as a percentage increase) than at most of the natural sites. Increases in NP will result in greater water yield to surface water and may accelerate the rate at which chemical constituents contained within mine waste are released to downstream receptors, suggesting these potential changes need to be considered in mine closure designs.

scholarly journals Content analysis for <i>Agricultural Records</i> of the United Kingdom, AD 200 to 1977: a study of frequency in human records concerning climate phenomenon

2011 ◽  
Vol 7 (4) ◽  
pp. 2555-2577
Author(s):  
D. H. Holt
Keyword(s):  
Content Analysis ◽  
Ice Age ◽  
Climate Data ◽  
The United Kingdom ◽  
Weather Phenomenon ◽  
Continuous Record ◽  
Frequency Occurrence ◽  
The Uk ◽  
Climate Events

Abstract. A content analysis has been completed on a text from the UK that has gathered agricultural and climate data from the years AD 220 to 1977 from 100s of sources. The content analysis coded all references to climate and agriculture to ascertain which climate events were recorded and which were not. This study addressed the question: is there bias in human records of climate? This evaluated the continuous record (AD 1654–1977), discontinous record (AD 220–1653), the whole record (AD 220–1977), the Little Climate Optimum (AD 850–1250) and the Little Ice Age (AD 1450–1880). This study shows that there is no significant variation in any of these periods in frequency occurrence of "good" or "bad" climate suggesting humans are not recording long-term changes in climate, but they are recording weather phenomenon as it occurs.

scholarly journals Climate Change Influences Basidiome Emergence of Leaf-Cutting Ant Cultivars

2021 ◽  
Vol 7 (11) ◽  
pp. 912
Author(s):  
Rodolfo Bizarria ◽  
Pepijn W. Kooij ◽  
Andre Rodrigues
Keyword(s):  
Climate Change ◽  
Climate Change Scenarios ◽  
Nest Architecture ◽  
Historical Records ◽  
Climate Data ◽  
Temperature And Precipitation ◽  
Data Analyses ◽  
Symbiotic Mutualism ◽  
Leaf Cutting

Maintaining symbiosis homeostasis is essential for mutualistic partners. Leaf-cutting ants evolved a long-term symbiotic mutualism with fungal cultivars for nourishment while using vertical asexual transmission across generations. Despite the ants’ efforts to suppress fungal sexual reproduction, scattered occurrences of cultivar basidiomes have been reported. Here, we review the literature for basidiome occurrences and associated climate data. We hypothesized that more basidiome events could be expected in scenarios with an increase in temperature and precipitation. Our field observations and climate data analyses indeed suggest that Acromyrmex coronatus colonies are prone to basidiome occurrences in warmer and wetter seasons. Even though our study partly depended on historical records, occurrences have increased, correlating with climate change. A nest architecture with low (or even the lack of) insulation might be the cause of this phenomenon. The nature of basidiome occurrences in the A. coronatus–fungus mutualism can be useful to elucidate how resilient mutualistic symbioses are in light of climate change scenarios.

Forecasting long-term shoreline evolution in highly antrhopized coastal areas

2021 ◽  
Author(s):  
Moisés Álvarez-Cuesta ◽  
Alexandra Toimil ◽  
Iñigo J. Losada
Keyword(s):  
Climate Models ◽  
Regional Scale ◽  
Mediterranean Coast ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Coastal Zones ◽  
Climate Data ◽  
Shoreline Evolution ◽  
Automated Technique

&lt;p&gt;A new numerical model for addressing long-term coastline evolution on a local to regional scale on highly anthropized coasts is presented. The model, named IH-LANS (Long-term ANthropized coastlines Simulation tool), is validated over the period 1990-2020 and applied to obtain an ensemble of end-of-century shoreline evolutions. IH-LANS combines a hybrid (statistical-numerical) deep-water propagation module and a shoreline evolution model. Longshore and cross-shore processes are integrated together with the effects of man-made interventions. For the ease of calibration, an automated technique is implemented to assimilate observations. The model is applied to a highly anthropized 40 km stretch located along the Spanish Mediterranean coast. High space-time resolution climate data and satellite-derived shorelines are used to drive IH-LANS. Observed shoreline evolution (&lt;10 meters of root mean square error, RMSE) is successfully represented while accounting for the effects of nourishments and the construction and removal of groynes, seawalls and breakwaters over time. Then, in order to drive the ensemble of end-of-century shoreline evolutions, wave and water level projections downscaled from different climate models for various emissions scenarios are employed to force the calibrated model. From the forecasted shoreline time-series, information from multiple time-scales is unraveled yielding valuable information for coastal planners. The efficiency and accuracy of the model make IH-LANS a powerful tool for management and climate change adaptation in coastal zones.&lt;/p&gt;

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