scholarly journals Spectrally Dependent CLARREO Infrared Spectrometer Calibration Requirement for Climate Change Detection

2017 ◽  
Vol 30 (11) ◽  
pp. 3979-3998 ◽  
Author(s):  
Xu Liu ◽  
Wan Wu ◽  
Bruce A. Wielicki ◽  
Qiguang Yang ◽  
Susan H. Kizer ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Vapor ◽  
Climate Model ◽  
Model Simulation ◽  
Atmospheric Temperature ◽  
Reanalysis Data ◽  
Natural Variability ◽  
Radiometric Calibration ◽  
Climate Trends ◽  
Moisture Profiles

Abstract Detecting climate trends of atmospheric temperature, moisture, cloud, and surface temperature requires accurately calibrated satellite instruments such as the Climate Absolute Radiance and Refractivity Observatory (CLARREO). Previous studies have evaluated the CLARREO measurement requirements for achieving climate change accuracy goals in orbit. The present study further quantifies the spectrally dependent IR instrument calibration requirement for detecting trends of atmospheric temperature and moisture profiles. The temperature, water vapor, and surface skin temperature variability and the associated correlation time are derived using the Modern-Era Retrospective Analysis for Research and Applications (MERRA) and European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The results are further validated using climate model simulation results. With the derived natural variability as the reference, the calibration requirement is established by carrying out a simulation study for CLARREO observations of various atmospheric states under all-sky conditions. A 0.04-K (k = 2; 95% confidence) radiometric calibration requirement baseline is derived using a spectral fingerprinting method. It is also demonstrated that the requirement is spectrally dependent and that some spectral regions can be relaxed as a result of the hyperspectral nature of the CLARREO instrument. Relaxing the requirement to 0.06 K (k = 2) is discussed further based on the uncertainties associated with the temperature and water vapor natural variability and relatively small delay in the time to detect for trends relative to the baseline case. The methodology used in this study can be extended to other parameters (such as clouds and CO2) and other instrument configurations.

scholarly journals Predicting hydrologic responses to climate changes in highly glacierized and mountainous region Upper Indus Basin

2020 ◽  
Vol 7 (8) ◽  
pp. 191957 ◽  
Author(s):  
Muhammad Izhar Shah ◽  
Asif Khan ◽  
Tahir Ali Akbar ◽  
Quazi K. Hassan ◽  
Asim Jahangir Khan ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Climate Model ◽  
Assessment Tool ◽  
Model Simulation ◽  
Global Climate ◽  
Meteorological Data ◽  
Swat Model ◽  
Indus Basin ◽  
Upper Indus Basin

The Upper Indus Basin (UIB) is a major source of supplying water to different areas because of snow and glaciers melt and is also enduring the regional impacts of global climate change. The expected changes in temperature, precipitation and snowmelt could be reasons for further escalation of the problem. Therefore, estimation of hydrological processes is critical for UIB. The objectives of this paper were to estimate the impacts of climate change on water resources and future projection for surface water under different climatic scenarios using soil and water assessment tool (SWAT). The methodology includes: (i) development of SWAT model using land cover, soil and meteorological data; (ii) calibration of the model using daily flow data from 1978 to 1993; (iii) model validation for the time 1994–2003; (iv) bias correction of regional climate model (RCM), and (v) utilization of bias-corrected RCM for future assessment under representative concentration pathways RCP4.5 and RCP8.5 for mid (2041–2070) and late century (2071–2100). The results of the study revealed a strong correlation between simulated and observed flow with R 2 and Nash–Sutcliff efficiency (NSE) equal to 0.85 each for daily flow. For validation, R 2 and NSE were found to be 0.84 and 0.80, respectively. Compared to baseline period (1976–2005), the result of RCM showed an increase in temperature ranging from 2.36°C to 3.50°C and 2.92°C to 5.23°C for RCP4.5 and RCP8.5 respectively, till the end of the twenty-first century. Likewise, the increase in annual average precipitation is 2.4% to 2.5% and 6.0% to 4.6% (mid to late century) under RCP4.5 and RCP8.5, respectively. The model simulation results for RCP4.5 showed increase in flow by 19.24% and 16.78% for mid and late century, respectively. For RCP8.5, the increase in flow is 20.13% and 15.86% during mid and late century, respectively. The model was more sensitive towards available moisture and snowmelt parameters. Thus, SWAT model could be used as effective tool for climate change valuation and for sustainable management of water resources in future.

scholarly journals Characterization of blackbody inhomogeneity and its effect on the retrieval results of the GLORIA instrument

2018 ◽  
Author(s):  
Anne Kleinert ◽  
Isabell Krisch ◽  
Jörn Ungermann ◽  
Albert Adibekyan ◽  
Berndt Gutschwager ◽  
...  
Keyword(s):  
Water Vapor ◽  
Temperature Distribution ◽  
Vertical Resolution ◽  
Radiometric Calibration ◽  
Climate Trends ◽  
Trace Species ◽  
Calibration Uncertainty

Abstract. Limb sounding instruments play an important role for the monitoring of climate trends, as they provide a good vertical resolution. Traceability to the SI via onboard reference or transfer standards is needed to compare trend estimates from multiple instruments. This study investigates the required uncertainty of these radiation standards to properly resolve decadal trends of climate relevant trace species like ozone, water vapor and temperature distribution for the Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA). Temperature nonuniformities of the onboard reference blackbodies, used for radiometric calibration, have an impact on the calibration uncertainty. The propagation of these nonuniformities through the retrieval is analyzed. A threshold for the maximum tolerable uncertainty of the blackbody temperature is derived, so that climate trends can be significantly identified with GLORIA.

scholarly journals Emerging climate signals in the Lena River catchment: a non-parametric statistical approach

2020 ◽  
Vol 24 (5) ◽  
pp. 2817-2839
Author(s):  
Eric Pohl ◽  
Christophe Grenier ◽  
Mathieu Vrac ◽  
Masa Kageyama
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Models ◽  
Climate Model ◽  
Natural Variability ◽  
Climate Signal ◽  
Lena River ◽  
River Catchment ◽  
Model Simulations ◽  
Non Parametric

Abstract. Climate change has far-reaching implications in permafrost-underlain landscapes with respect to hydrology, ecosystems, and the population's traditional livelihoods. In the Lena River catchment, eastern Siberia, changing climatic conditions and the associated impacts are already observed or expected. However, as climate change progresses the question remains as to how far we are along this track and when these changes will constitute a significant emergence from natural variability. Here we present an approach to investigate temperature and precipitation time series from observational records, reanalysis, and an ensemble of 65 climate model simulations forced by the RCP8.5 emission scenario. We developed a novel non-parametric statistical method to identify the time of emergence (ToE) of climate change signals, i.e. the time when a climate signal permanently exceeds its natural variability. The method is based on the Hellinger distance metric that measures the similarity of probability density functions (PDFs) roughly corresponding to their geometrical overlap. Natural variability is estimated as a PDF for the earliest period common to all datasets used in the study (1901–1921) and is then compared to PDFs of target periods with moving windows of 21 years at annual and seasonal scales. The method yields dissimilarities or emergence levels ranging from 0 % to 100 % and the direction of change as a continuous time series itself. First, we showcase the method's advantage over the Kolmogorov–Smirnov metric using a synthetic dataset that resembles signals observed in the utilized climate models. Then, we focus on the Lena River catchment, where significant environmental changes are already apparent. On average, the emergence of temperature has a strong onset in the 1970s with a monotonic increase thereafter for validated reanalysis data. At the end of the reanalysis dataset (2004), temperature distributions have emerged by 50 %–60 %. Climate model projections suggest the same evolution on average and 90 % emergence by 2040. For precipitation the analysis is less conclusive because of high uncertainties in existing reanalysis datasets that also impede an evaluation of the climate models. Model projections suggest hardly any emergence by 2000 but a strong emergence thereafter, reaching 60 % by the end of the investigated period (2089). The presented ToE method provides more versatility than traditional parametric approaches and allows for a detailed temporal analysis of climate signal evolutions. An original strategy to select the most realistic model simulations based on the available observational data significantly reduces the uncertainties resulting from the spread in the 65 climate models used. The method comes as a toolbox available at https://github.com/pohleric/toe_tools (last access: 19 May 2020).

scholarly journals Intensification of Precipitation Extremes with Warming in a Cloud-Resolving Model

2011 ◽  
Vol 24 (11) ◽  
pp. 2784-2800 ◽  
Author(s):  
Caroline J. Muller ◽  
Paul A. O’Gorman ◽  
Larissa E. Back
Keyword(s):  
Water Vapor ◽  
Climate Model ◽  
Atmospheric Temperature ◽  
Precipitation Extremes ◽  
Temperature Structure ◽  
Precipitation Efficiency ◽  
Fractional Rate ◽  
Cloud Resolving Model ◽  
Climate Model Simulations ◽  
Static Energy

Abstract A cloud-resolving model is used to investigate the effect of warming on high percentiles of precipitation (precipitation extremes) in the idealized setting of radiative-convective equilibrium. While this idealized setting does not allow for several factors that influence precipitation in the tropics, it does allow for an evaluation of the response of precipitation extremes to warming in simulations with resolved rather than parameterized convection. The methodology developed should also be applicable to less idealized simulations. Modeled precipitation extremes are found to increase in magnitude in response to an increase in sea surface temperature. A dry static energy budget is used to relate the changes in precipitation extremes to changes in atmospheric temperature, vertical velocity, and precipitation efficiency. To first order, the changes in precipitation extremes are captured by changes in the mean temperature structure of the atmosphere. Changes in vertical velocities play a secondary role and tend to weaken the strength of precipitation extremes, despite an intensification of updraft velocities in the upper troposphere. The influence of changes in condensate transports on precipitation extremes is quantified in terms of a precipitation efficiency; it does not change greatly with warming. Tropical precipitation extremes have previously been found to increase at a greater fractional rate than the amount of atmospheric water vapor in observations of present-day variability and in some climate model simulations with parameterized convection. But the fractional increases in precipitation extremes in the cloud-resolving simulations are comparable in magnitude to those in surface water vapor concentrations (owing to a partial cancellation between dynamical and thermodynamical changes), and are substantially less than the fractional increases in column water vapor.

scholarly journals A model study of the energy and mass balance of Chhota Shigri glacier in the Western Himalaya, India

2011 ◽  
Vol 5 (1) ◽  
pp. 95-129 ◽  
Author(s):  
F. Pithan
Keyword(s):  
Climate Change ◽  
Mass Balance ◽  
Climate Model ◽  
Monsoon Season ◽  
Regional Climate ◽  
Western Himalaya ◽  
Reanalysis Data ◽  
Balance Model ◽  
Mass Balances ◽  
The Impact

Abstract. The impact of climate change on Himalaya mountain glaciers is increasingly subject of public and scientific debate. However, observational data are sparse and important knowledge gaps remain in the understanding of what drives changes in these glaciers' mass balances. The present study investigates the glacier regime on Chhota Shigri, a benchmark glacier for the observation of climate change in the monsoon-arid transition zone of Western Himalaya. Results of an energy-balance model driven by reanalysis data and the observed mass balances from three years on 50 m altitude intervals across the glacier display a correlation coefficient of 0.974. Contrary to prior assumptions, monsoon precipitation accounts for a quarter to a third of total accumulation. It has an additional importance because it lowers the surface albedo during the ablation season. Results confirm radiation as the main energy source for melt on Himalaya glaciers. Latent heat flux acts as an important energy sink in the pre-monsoon season. Mass balance is most sensitive to changes in atmospheric humidity, changing by 900 mm w.e. per 10% change in humidity. Temperature sensitivity is 220 mm w.e.K−1. Model results using 21st century anomalies from a regional climate model based on the SRES A2 scenario suggest that a monsoon increase might offset the effect of warming.

scholarly journals Timing of human-induced climate change emergence from internal climate variability for hydrological impact studies

2018 ◽  
Vol 49 (2) ◽  
pp. 421-437 ◽  
Author(s):  
Mei-Jia Zhuan ◽  
Jie Chen ◽  
Ming-Xi Shen ◽  
Chong-Yu Xu ◽  
Hua Chen ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Climate Models ◽  
Climate Model ◽  
Climate Trends ◽  
Impact Studies ◽  
Internal Climate Variability ◽  
Temperature And Precipitation ◽  
Hydrological Impact ◽  
Near Term

Abstract This study proposes a method to estimate the timing of human-induced climate change (HICC) emergence from internal climate variability (ICV) for hydrological impact studies based on climate model ensembles. Specifically, ICV is defined as the inter-member difference in a multi-member ensemble of a climate model in which human-induced climate trends have been removed through a detrending method. HICC is defined as the mean of multiple climate models. The intersection between HICC and ICV curves is defined as the time of emergence (ToE) of HICC from ICV. A case study of the Hanjiang River watershed in China shows that the temperature change has already emerged from ICV during the last century. However, the precipitation change will be masked by ICV up to the middle of this century. With the joint contributions of temperature and precipitation, the ToE of streamflow occurs about one decade later than that of precipitation. This implies that consideration for water resource vulnerability to climate should be more concerned with adaptation to ICV in the near-term climate (present through mid-century), and with HICC in the long-term future, thus allowing for more robust adaptation strategies to water transfer projects in China.

scholarly journals Local impact analysis of climate change on precipitation extremes: are high-resolution climate models needed for realistic simulations?

2016 ◽  
Author(s):  
Hossein Tabari ◽  
Rozemien De Troch ◽  
Olivier Giot ◽  
Rafiq Hamdi ◽  
Piet Termonia ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Spatial Resolution ◽  
Impact Analysis ◽  
Climate Models ◽  
Climate Model ◽  
Daily Precipitation ◽  
Reanalysis Data ◽  
Precipitation Extremes ◽  
Added Value

Abstract. This study explores whether climate models with higher spatial resolution provide higher accuracy for precipitation simulations and/or different climate change signals. The outputs from two convection-permitting climate models (ALARO and CCLM) with a spatial resolution of 3–4 km are compared with those from the coarse scale driving models or reanalysis data for simulating/projecting daily and sub-daily precipitation quantiles. The high-resolution ALARO and CCLM models reveal an added value to capture sub-daily precipitation extremes during summer compared to the driving GCMs and reanalysis data. Further validation of historical climate simulations based on design precipitation statistics derived from intensity–duration–frequency (IDF) curves shows a better match of the convection-permitting model results with the observations-based IDF statistics. Results moreover indicate that one has to be careful in assuming spatial scale independency of climate change signals for the delta change downscaling method, as high-resolution models may show larger changes in extreme precipitation. These larger changes appear to be dependent on the climate model, since such intensification is not observed for the ALARO model.

scholarly journals Origin and Propagation of a Disturbance Associated with an African Easterly Wave as a Precursor of Hurricane Alberto (2000)

2005 ◽  
Vol 133 (11) ◽  
pp. 3276-3298 ◽  
Author(s):  
Yuh-Lang Lin ◽  
Katie E. Robertson ◽  
Christopher M. Hill
Keyword(s):  
Water Vapor ◽  
Climate Model ◽  
Regional Climate ◽  
Reanalysis Data ◽  
Stationary Mode ◽  
Moist Convection ◽  
Convective Systems ◽  
African Easterly Wave ◽  
Easterly Wave ◽  
Mesoscale Convective

Abstract In this study, it is proposed that mesoscale convective complexes (MCCs) and a mesovortex (MV) were embedded within a wavelike disturbance over North Africa that led to the genesis of Hurricane Alberto (2000). The wavelike disturbance observed may be classified as an African easterly wave (AEW). Based on the cloud-top area and brightness values observed from infrared satellite data, four genesis and three lysis stages are identified within a cycle of moist convection associated with the pre-Alberto disturbance. The availability of water vapor is the most essential factor controlling the convective cycle of the pre-Alberto disturbance over the African continent. The presence of significant topography also contributes to the generation or decay of the associated MCCs through regulation of the water vapor supply. Further analysis of Meteosat satellite imagery reveals that the incipient disturbances for 23 of 34 eastern Atlantic tropical cyclones originated from the Ethiopian highlands (EH) region during the period of 1990–2001. The pre-Alberto disturbance was found to have exhibited characteristics of an AEW. At the EH, there existed two modes of disturbance development: a stationary mode and a propagating mode. The stationary mode corresponded with the generation of moist convection over the EH triggered by diurnally variant sensible heating, while the propagating mode corresponded with the generation and propagation of MVs and mesoscale convective systems (MCSs) from the lee side of the EH over a period of about 2 to 3 days. These components of the disturbance propagated westward together within an AEW train at an average speed of 11.6 m s−1. The average wavelength was roughly estimated to be about 2200 km. To prove that disturbances generated at the EH are indeed AEWs, the NCAR Regional Climate Model Version 3.0 is adopted to simulate the event. The simulated fields showed that both the propagating wave and stationary mountain wave modes were present, the convection was generated over the EH, and the pre-Alberto disturbance was generated near the lee of the EH. In addition, the convective cycle detected from NCEP reanalysis data was also reflected in the simulated fields. The simulated AEW possesses similar wave characteristics as the observed pre-Alberto disturbance.

System Design and Evaluation of Coupled Ensemble Data Assimilation for Global Oceanic Climate Studies

2007 ◽  
Vol 135 (10) ◽  
pp. 3541-3564 ◽  
Author(s):  
S. Zhang ◽  
M. J. Harrison ◽  
A. Rosati ◽  
A. Wittenberg
Keyword(s):  
Twentieth Century ◽  
Data Assimilation ◽  
Climate Variability ◽  
Climate Model ◽  
Heat Content ◽  
Coupled Model ◽  
Atmospheric Temperature ◽  
Meridional Overturning Circulation ◽  
Climate Trends ◽  
Fully Coupled

Abstract A fully coupled data assimilation (CDA) system, consisting of an ensemble filter applied to the Geophysical Fluid Dynamics Laboratory’s global fully coupled climate model (CM2), has been developed to facilitate the detection and prediction of seasonal-to-multidecadal climate variability and climate trends. The assimilation provides a self-consistent, temporally continuous estimate of the coupled model state and its uncertainty, in the form of discrete ensemble members, which can be used directly to initialize probabilistic climate forecasts. Here, the CDA is evaluated using a series of perfect model experiments, in which a particular twentieth-century simulation—with temporally varying greenhouse gas and natural aerosol radiative forcings—serves as a “truth” from which observations are drawn, according to the actual ocean observing network for the twentieth century. These observations are then assimilated into a coupled model ensemble that is subjected only to preindustrial forcings. By examining how well this analysis ensemble reproduces the “truth,” the skill of the analysis system in recovering anthropogenically forced trends and natural climate variability is assessed, given the historical observing network. The assimilation successfully reconstructs the twentieth-century ocean heat content variability and trends in most locations. The experiments highlight the importance of maintaining key physical relationships among model fields, which are associated with water masses in the ocean and geostrophy in the atmosphere. For example, when only oceanic temperatures are assimilated, the ocean analysis is greatly improved by incorporating the temperature–salinity covariance provided by the analysis ensemble. Interestingly, wind observations are more helpful than atmospheric temperature observations for constructing the structure of the tropical atmosphere; the opposite holds for the extratropical atmosphere. The experiments indicate that the Atlantic meridional overturning circulation is difficult to constrain using the twentieth-century observational network, but there is hope that additional observations—including those from the newly deployed Argo profiles—may lessen this problem in the twenty-first century. The challenges for data assimilation of model systematic biases and evolving observing systems are discussed.

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