Research Proposal Submitted To: NOAA CPO

Research Proposal Submitted To: NOAA CPO

E. Crosman:Improving Long-term Records of High-Resolution Satellite-derived Lake Surface Temperature for Global and Regional Climate Studies

Research Proposal Submitted to: NOAA CPO

NOAA (R/CP1), SSMC3, Room 12734, Silver Spring, MD 20910

Improving Long-term Records of High-Resolution Satellite-derived Lake Surface Temperature for Global and Regional Climate Studies

Federal Funding Opportunity Number: NOAA-OAR-CPO-2018-2005133

Competition: CPO – Ocean Observing and Monitoring Program (OOM)

Erik Crosman

Research Assistant Professor

Department of Atmospheric Sciences

135 S 1460 E, Room 819 WBB
Salt Lake City, UT 84112-0102

University of Utah

Phone: 505 570-0552; Fax: 801 581-4262

Email:

Institutional Representative: Erica Trejo

Office of Sponsored Projects, University of Utah

1471 E Federal Way

Salt Lake City, UT 84102

801 581-6232; 801-581-3007 (fax)

Funds requested: Total

Year 1 135,617; Year 2 $132,807; Total $268,424

Proposed project period 1 July 2018-30 June 2020

Funds requested for University of Utah:

Year 1 135,617; Year 2 $132,807; Total $268,424

Improving Long-term Records of High-Resolution Satellite-derived Lake Surface Temperature for Global and Regional Climate Studies

Federal Funding Opportunity Number: NOAA-OAR-CPO-2018-2005133

Competition: CPO- Ocean Observing and Monitoring Program (OOM)

PI: Erik Crosman, University of Utah

Abstract(not to exceed one page)

Lakes are a critical component of the earth system and are known as “sentinels” of climate change. Lake surface water temperature (LSWT) is an important parameter for quantifying and modeling regional responses to climate change and LSWT has been shown to have impacts on regional climate. However, considerable uncertainty in the trends calculated from existing satellite-derived LSWT exists, and global climate models do not resolve many lakes and/or do not use appropriate surface temperature forcing data sets to prescribe LSWT in retrospective climate simulations. The proposed work will utilize satellite-derived lake temperature datasets from two state-of-the-art global climate SST products (NOAA Pathfinder V5.3 and NASA MODIS LST V) to produce new quality-controlled LSWT climatological and trend data sets with uncertainty estimates between 1985-preent for use by global climate change modeling and observational studies.

The summary of the work to be completed is threefold:

1)Produce a new, improved global lake temperature mean climatology data set as well as a long time series trend dataset for hundreds of lakes for use in climate change observational and numerical modeling studies derived from the NOAA Pathfinder V5.3 and NASA MODIS LST data sets, with a focus on both cold-season (when clouds limit number of available observations) and smaller lakes (5-30 km in diameter), as daily time series of these products are currently unavailable.

2)Determine the ability of satellite-derived lake temperature datasets to evaluate global trends in lake surface temperature between 1985-present. Rigorous in situ validation of satellite retrievals will be conducted for numerous lakes over long periods of record, which has not been previously conducted.

3)Apply improved cloud masking and other statistical quality-control algorithms to produce an improved lake temperature time series, and quantify the potential impact of errors and uncertainties associated with cloud contamination and temporal gaps (long cloudy periods) on satellite-derived lake temperature trends.

The proposed research targets the following areas in the Ocean Observing and Monitoring FY18 program call for proposals: (1) Development of data sets for the climate research community and (2) Projects that develop or improve datasets suitable for periodically updated assessments or monitoring products for weather and climate extremes and impacts on water resources. This work will provide the basis for improving existing as well as providing new climate data for satellite-derived LSWT data sets, and will produce targeted research results that willimprove uncertainty in the error characteristics of satellite-derived LSWT retrievals for use in long-term monitoring and climate modeling. In terms of NOAA’s long term climate goals, this proposal will advance climate intelligence and resilience through providing a data set useful for addressing (1) weather and climate extremes, (2) climate impacts on water resources, and the (3) sustainability of marine ecosystems.

Results from Prior Research

  1. Multi-sensor Improved Sea Surface Temperatures (MISST2): 2013-2017 [PI E. Crosman; Co-PI J. Horel]

This project, which ends in December 2017, is being supported by the NASA Integrated Ocean Observing System (IOOS) program. The goal of this study was to provide recommendations for improving lake temperatures for input into numerical weather prediction models, and to review the error sources and uncertainties associated with lake temperature retrievals, as well as the various split-window and other algorithms that have been developed for satellite-derived lake temperature over the past few decades. As part of this study, an evaluation of the NASA Multi-scale Ultra-high Resolution (MUR) analysis of lake surface temperature for several lakes was conducted, with results recently published in Crosman et al. (2017a). Statistical and other quality-control algorithms specifically designed for improving lake temperature retrievals from satellite have been developed at the onset of this study and were presented by Grim et al. (2013). An extensive review paper on lake temperature (Crosman et al. 2017b) has been submitted.

  1. NMP Data Hub. 2017-2018 [PI E. Crosman]

As part of this program, the University of Utah provides a wide range of weather data under the National Mesonet Program for use by the National Weather Service, public, and private sectors, and for input into the National Centers for Environmental Prediction Meteorological Assimilation Data Ingest System (MADIS) for initializing a suite of numerical weather models. As part of this project, a network of weather stations are maintained in northern Utah as part of this project, including 14 surface stations measuring wind, temperature, humidity, solar radiation, atmospheric pressure, precipitation. Several wind sodars and backscatter ceilometers for profiling the boundary-layer are also operated on a routine basis as part of this project. The data for this project are all available via a state-of-the art API:

This includes a number of in situ buoy measurements of lake temperature that will be utilized in the proposed study.

  1. Improve Air Quality Modeling for the Wasatch Front & Cache Valley Winter Air Pollution Episodes: 2015-2016 [PI E. Crosman]

The Utah DAQsupported a modeling study to develop appropriate modeling strategies to simulate the meteorological conditions associated with poor winter air quality episodes in Utah basins. This work builds on the prior modeling work for similar conditions in the Uintah Basin (Neemann et al. 2014) as well as other wintertime modeling studies for the Salt Lake region (Lareau and Horel 2015a,b). The simulated meteorological conditions are sensitive to the specification of the land and lake surface state, e.g., areal extent and temperature of the Great Salt Lake and surrounding snow cover. The results of this research were published by Foster et al. (2017).

  1. 2015 Great Salt Lake Summer Ozone Study: 2014-2016 [PI J. Horel; Co-PI E. Crosman]

The Utah Division of Air Quality (DAQ) supported a field study in 2015 to improve understanding of the temporal and spatial distribution of ozone in the vicinity of the metropolitan regions of northern Utah (Horel et al. 2016). During the 2015 summer months, DAQ staff and researchers from the University of Utah, Utah State University, and Weber State University deployed ozone sensors near the Great Salt Lake at fixed sites as well as on a UTA TRAX light rail car, vehicles, UAV’s, and tethered balloons. An ozone sensor onboard the KSL traffic helicopter (Crosman et al. 2017) provided critical information on ozone concentration aloft, particularly along major traffic corridors during the late afternoon when ozone levels reach their peak. The temperature of the Great Salt Lake derived from satellite (Blaylock et al. 2017) was found to be a contributing factor in the ozone pollution levels associated with the lake breeze front. The data from this project are publically accessible at the following website:

Statement of Work

I.Overview

1.Problem Statement

Our proposed research focuses on providing a new long-term and quality-controlled satellite-derived lake surface temperature climatological dataset between 1985-present for use by the climate sciences for a wide range of applications. Only sparse in situ observations of lake temperatures exist, primarily in European and North American lakes. Consequently, long time series of satellite-derived lake temperature with global coverage is of high potential value for a wide ranges of geophysical applications and climate studies. Satellite-derived lake surface water temperature (LSWT) estimates are generally more uncertain and unavailable to the scientific and general public than oceanic sea surface temperature (SST) retrievals (Oesch et al. 2008; Hulley et al. 2011; Fiedler et al. 2014). In addition, no comprehensive global quality-controlled and extensively validatedclimatological global data sets of daily LSWT for small lakes (<25 km in diameter) exists. Evaluation of several recent climatological studies of LSWT for (mostly) large lakes (Hook et al. 2012; Layden et al. 2015; O’Reilly et al. 2015) indicates uncertainty and in some cases opposite climate trends deduced from the various LSWT climate studies, suggesting that documented problems with cloud contamination (Oesch et al. 2008; Politi et al. 2012) and temporal gap errors (Crosman et al. 2017a) may be introducing uncertainty in the existing analyses of global LSWT trends on these medium-sized to large lakes, These differences indicate the need to evaluate these products globally and toapply quality-control measures to mitigate the potential impacts of cloud contamination and temporal gap errors on satellite-derived LSWT trend analysis and LSWT climatological data sets.

2.Scientific Objectives

In this study, we will develop and implement cloud clearing and statistical quality control algorithms for use with satellite-derived lake temperature datasets from two state-of-the-art global climate SST products (NOAA Pathfinder V5.3 and NASA MODIS LST V) and then derive new data sets of quality-controlled LSWT climatological annual cycles in LSWT and trend daily time series with associated error and uncertainty estimates for use by global climate change modeling and observational studies. The relevancy of this data will only increase as regional climate modeling studies are run at increasingly high spatial resolution. The MODIS radiometer (2000-present) has both the high-resolution capable of resolving smaller lakes and now a sufficient period of record (2000-present) to resolve climate trends in “smaller” lakes between 4 and 20 km in diameter. These smaller lakes have not been rigorously studied or validated to our knowledge on a global scale in previous studies. In addition, the latest version of NOAA Pathfinder V5.3 (1981-2014) that was recently released includes a number of improvements that likely improve data availability over larger small lakes and over some medium-sized lakes that have not been previously analyzed (16-40 km in diameter). In this study we will also rigorously validate both the pre-processed and post-quality control LSWT datasets against in situ observations on a number of lakes. More rigorous validation over long time periods and over small lakes between in situ and satellite-derived LSWT is needed, as most lake validations were conducted over large lakes and temporally short periods of time (Crosman et al., 2017b).

3.Task Statement Relevancy

We will address two of the three of the research needs identified by the FY18 Ocean Observing and Monitoring Division call for “High-quality data sets for enhancing predictions and informing stakeholders: (1) Development of data sets for the climate research community and (2) Projects that develop or improve datasets suitable for periodically updated assessments or monitoring products for weather and climate extremes and impacts on water resources. Restated in the context of our proposed research, this research will (1) Develop improved LSWT data sets for the climate research and modeling community, and (2) develop and improve by rigorously validating and quality-controlling available satellite-derived LSWT records to assess the impacts of changes in global LSWT for climate, weather, and water resources.

The time series and climatology of LSWT produced by this study will directly address NOAA’s long term climate goals to advance climate intelligence and resilience through providing a data set useful for addressing (1) weather and climate extremes, (2) climate impacts on water resources, and the (3) sustainability of marine ecosystems.

4.Benefits

Figure 1. Key application areas of satellite-derived lake surface temperature

The PI has a demonstrated record in evaluating lake surface temperature analyses and trends from satellite that provide benefit to the research and operational communities (Crosman and Horel 2009; Grim et al. 2013; Crosman et al. 2017a, b). Our research will provide the largest benefits to the general public and scientific community by improving both the accessibility and quality of available lake surface temperature data, which provides valuable information on lake state for a wide range of geophysical applications that are sensitive to global climate and climate change (Fig. 1). Currently, much of the climate and limnological community does not view or utilize satellite-derived lake temperature as it is not an easily-accessible product and documentation does not exist (many users do not realize that oceanic SST data sets are also generated over inland waters), unlike the highly accessible nature of numerous SST products for the wide oceanographic community. Deliverables will be focused on providing new derived data sets of quality-controlled LSWT climatological and trend daily time series for the large community that would benefit from the data. The deliverables will also include at least two peer-reviewed research articles and webinars and presentations in national and regional venues oriented to the climatological, limnological, biological, and numerical weather prediction and climate communities. In addition, the outcomes of our research will help develop recommendations for future enhanced reprocessing with lake-specific split-window algorithms of the entire period of satellite record of temperature of inland waters in terms of appropriate quality flags, cloud masks, and statistical tests to filter residual cloud contamination by thin cirrus.

II. Technical and Scientific Background

Lakes worldwide provide many far-reaching benefits to society, including drinking and agricultural water, fishery habitat, recreational opportunities, transportation routes, and hydroelectric energy (Stenderra et al. 2012; Dornhoffer and Oppelt 2016) (Fig. 1). As an integral component of the earth system, lakes have been found to be “sentinels” of climate change (Adrian et al., 2009; MacKay et al., 2009; Williamson et al., 2009; Castendyk et al., 2016).

Approximately 117 million lakes worldwide cover 3.7% of the non-glaciated planetary land surface area and comprise a total volume of ~200,000 km3, with over 17,000 lakes worldwide with surface areas greater than 10 km2 (Verpoorter et al., 2014; Cael et al., 2017). Satellite remote sensing of lakes can provide valuable information on lake water transparency, biota, hydrology, temperature, and ice phenology (Dornhoffer and Oppelt 2016).

Figure 2. High-resolution analysis at 1 km of Lake Temperature by the NASA MUR analysis (see Crosman et al, 2017a, Chin et al. 2017) in four global sub-regions with high density of small to medium-sized lakes. Imagery courtesy of NASA State of The Ocean

Lake surface temperature is an important parameter for understanding and modeling the biology, hydrology, weather and climate of lacustrine and adjoining terrestrial environments (e.g., Dutra et al. 2010; Kraemer et al. 2016; Reavie et al. 2016; Javaheri et al. 2016; Mason et al. 2016)(Fig. 1). Lake surface water temperature (LSWT) retrievals from satellite thermal infrared (TIR) sensors onboard numerous satellite platforms provide a spatially comprehensive dataset for lakes in the absence of clouds, and are the primary means used to obtain LSWT over thousands of lakes worldwide where in situ temperature measurements are unavailable (e.g., Fig. 2). Remote sensing of LSWT began in the 1980’s and has increased steadily in subsequent decades. However, satellite-derived LSWT estimates are generally more uncertain and unavailable to the scientific and public than oceanic sea surface temperature (SST) retrievals due to an increased difficulty in correcting for continental atmospheric air masses, lake elevation, cloud contamination, and shoreline effects (Hulley et al. 2011). In addition, gaps in the available satellite images due to clouds, the small size of many lakes (too small to be measured by many satellite infrared sensors) and relatively sparse in situ observations make it difficult to obtain imagery during cloudy and cool seasons in the mid-latitudes, and to validate these satellite-derived LSWT and to provide calibrated long-term spatially- and temporally-consistent LSWT analyses.

LSWT is also a critical input variable for numerical weather, climate, and hydrological models (e.g., Dutra et al. 2010; Balsamo et al. 2012; Kheyrollah Pour et al. 2014; Javaheri et al. 2016). While extensiveclimatological data sets and analyses of satellite-derived sea surface temperature (SST) are available and used in a wide range of applications, limited satellite-derivedLSWT climatological data sets exist, and the quality of these retrievals is impacted by difficult to remove thin cloud cover, as well as a number of other challenges including lake elevation, variations in atmospheric profiles of temperature and moisture, and dust and aerosol (Crosman and Horel 2009; Hulley et al. 2011; MaCallum and Merchant 2012). No climatological data set utilizing high-resolution daily MODIS satellite data currently exists for prescribing the temperature trends of thousands of small lakes (5-25 km in diameter) worldwide, despite the known impacts of these water bodies on regional climate and weather. Only limited regional climatologies of small lakes have been conducted. The most recent ARC-Lake version 3 data set has been extended to include smaller lakes, reservoirs, and ephemeral lakes ( However, this climatology is restricted by the ~weekly availability of satellite data from the Along-Track Scanning Radiometer (ATSR) imagery utilized in the ARC-Lake climatology. Only regional climatologies have been processed thus far with daily MODIS satellite data. For example, a recent study by Wan et al. (2017) derived a MODIS climatology between 2001 and 2014 for lake temperature for 374 lakes on the Tibetan plateau with areas greater then 10 km2, illustrating the feasibility of extending a similar approach globally as proposed for this study, while Riffler et al. (2015) derived a climatology for 25 larger lakes using local area coverage AVHRR data in lakes in the European Alps. The only global lake climatology currently available for ~200 of the (mostly) larger lakes in the world is the ARC-Lake climatology (MacCallum and Merchant 2012; Layden et al. 2015). Because of the frequent cloud cover in many mid-latitude regions, the lack of a ‘background’ climatological lake temperature product is a major limiting factor in the ability of weather and climate models to properly represent the surface state in regions such as Canada, the Tibetan Plateau, and the Rift Valley of Africa (Fig. 2). In this study, we propose to generate a high-resolution climatology for lakes which will directly address this problem.