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Caspian Sea level (CSL) has undergone substantial fluctuations during the past several hundred years. The causes over the entire historical period are uncertain, but we investigate here large changes seen in the past several decades. We use climate model‐predicted precipitation (P), evaporation (E), and observed river runoff (R) to reconstruct long‐term CSL changes for 1979–2015 and show that PER (P‐E + R) flux predictions agree very well with observed CSL changes. The observed rapid CSL increase (about 12.74 cm/yr) and significant drop (~−6.72 cm/yr) during the periods 1979–1995 and 1996–2015 are well accounted for by integrated PER flux predictions of ~+12.38 and ~−6.79 cm/yr, respectively. We show that increased evaporation rates over the Caspian Sea play a dominant role in reversing the increasing trend in CSL during the past 37 years. The current long‐term decline in CSL is expected to continue into the foreseeable future, under global warming scenarios.

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Wet troposphere corrections to altimeter measurements calculated from the TOPEX/Poseidon (T/P) Microwave Radiometer (TMR) and the ERS-1 and ERS-2 Microwave Radiometers (EMRland EMR2) are compared to each other and to European Centre for Medium-Range Weather Forecasts (ECMWF) model data. The most recently published correction algorithm for the EMR1 data [Stum et al., 1998] is applied. The suggested drift correction for TMR data [Keihm et al ., 1998, 2000] is also evaluated. The corrected EMR1 data (1991)(1992)(1993)(1994)(1995)(1996) produce a global (to +66 ø the T/P latitude range) long-term mean wet troposphere correction 6 and 13 mm lower than TMR and ECMWF, respectively. The EMR2 data (1995-1999) yield a mean wet troposphere correction 2 and 9 mm lower than TMR and ECMWF, respectively. After removing mean differences all three microwave radiometers reproduce similar long-term zonal wet troposphere corrections compared to the ECMWF model (10-14 mm rms) and to each other (5-9 mm rrns) with some zonally periodic differences, most < 10 mm. The ECMWF model shows variations compared to the radiometers of over 30 mm before 1995, about 20-30 mm from 1995 to 1997, and up to 20 mm from 1998 to 1999. The intersatellite differences include a latitudinally dependent annual signal, reaching 10 mm in amplitude. Before correcting for the TMR drift there exists a global relative TMR-EMR1 drift of-l.6 + 0.4 mm y-l, from 1992 to 1996. After correcting for the TMR drift the TMR-EMR1 trend is reduced to -0.4 + 0.2 mm y-l, supporting the TMR drift correction. The TMR-EMR2 trend changes sign (direction) after an anomaly that occurred in one of the EMR2 brightness temperatures during June 1996. Before the anomaly, with (without) the TMR correction the relative TMR-EMR2 trend is-3.0 + 1.9 (-4.0 + 2.1) mm again supporting the TMR drift correction. After the anomaly, through 1997 the TMR-EMR trend is 3.7 + 1.2 mm y-l, and from 1998 to 1999 it is 0.8 + 0.6 mm altimetry the measurement of a MSL signal of 1-2 mm y-• accuracy requires knowledge of the radiometer stability to better than 1 mm y-•. Additionally, for a satellite altimeter to produce a l-cm rms single-shot measurement of sea surface height the correction errors of the altimeter range, including the wet troposphere correction, need to be <1 cm. In addition to interradiometer comparisons, assessment of radiometer accuracy may be conducted by comparison with atmospheric general circulation models (e.g., European Centre for Medium-Range Weather Forecasts (ECMWF) or National Centers for Environmental Prediction (NCEP) data), observing errors that otherwise could not be characterized in time and geographical regions with other restricted analyses, such as radiosondes (e.g., during radiometer calibrations) or GPS [e.g., Haines and Bar-Sever, 1998]. Hence, when we once calibrated and evaluated the radiometers at the centimeter level and/or with small samples of data [cf.The tropospheric path delay is the largest correction for altimeter measurements. It can be divided into dry and wet...

GRACE and GRACE-FO mission data is utilized to assess mass flux derived from the North American Regional Reanalysis (NARR) and the NLDAS-2 Noah land surface model via multiple water balance formulations. Water balances are computed for 18 medium size basins in North America at the USGS Watershed Boundary Dataset HU2 level over the span of the GRACE and GRACE-FO missions (2002-2021). Performance of model-derived mass flux is presented in the context of statistical agreement to changes in terrestrial water storage (ΔTWS) derived from CSR GRACE RL06 mascons, and GRACE and NARR uncertainty is estimated against comparable datasets. The land surface water balance method utilizing NLDAS-2 Noah consistently outperforms the total column method utilizing NARR, which is likely due to enhanced precipitation forcing and an updated Noah model version used in NLDAS-2. The surface approach to the calculation of atmospheric moisture flux divergence is carried through the presented analyses, and is demonstrated to be comparable in performance to the more common volume approach. Mass balance methodology, basin characteristics, and ΔTWS signal characteristics are assessed to quantify effects on model performance and while factors such as basin size, basin average topography gradient, and ΔTWS annual amplitude are shown to have a measurable effect on model performance, no single factor exhibited a dominant or consistent effect. Drought conditions are shown to have a significant temporally localized effect on model-derived mass flux accuracy, with NARR being particularly susceptible to this effect.

&lt;p&gt;GRACE mission data is used to derive variations in terrestrial water storage in order to evaluate approaches to the water balance. The data in the span of the GRACE and GRACE Follow-On missions is analyzed, and long-term behavior of a variety of basins is characterized. Terrestrial water storage variations are calculated via a combination of flux quantities from land surface models and atmospheric reanalyses using two common water balance approaches as well as a third approach using a novel algorithm for basin boundary discretization. Results are used to evaluate the new approach and form an understanding of its limitations, in relation to both the model data ingested as well as the characteristics of the regions in question. &amp;#160;From the results, we observe significant variations in model performance over diverse geography and climatic conditions, such as diminished accuracy in atmospheric reanalyses under the effect of long term drought. These observations suggest utility as a diagnostic to assess and inform improvement in the studied models.&lt;/p&gt;