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Devils Lake Essay, Research Paper

Dramatic Fluctuations of Devils Lake, North Dakota:

Climate Connections and Forecasts

Connely K. Baldwin and Upmanu Lall

Utah Water Research Laboratory, Utah State University, Logan, UT 84322-8200

Introduction

The recent (1992-date) record rise in the level of the Devils Lake, North Dakota, has led to a number of questions as to the nature of regional and global climate variability, and the utility of existing methods for forecasting lake levels and assessing the associated flood risk. A purpose of the work presented here was to explore the connection of the Devils Lake volumetric fluctuations to interannual and longer regional and global climate fluctuations, and to test the performance of recently proposed time series forecasting methods. Wiche and Vecchia (1995), and Osborne (1998) provide background information on the lake, and prior forecasting and climate analysis. Key trends in hydroclimatic variables in the Devils Lake region are first identified and discussed in the context of large-scale hydroclimate variations. Hypotheses as to operative climatic mechanisms that have led to the recent rise in the lake level are developed from this analysis. Two types of long-range lake forecasts are then considered. A forecast of lake levels for the near future (1-5 years or an inter-annual period) is developed for assessing the potential of continued flooding and associated needs for disaster relief. Second, since closed basin lakes typically exhibit long memory, procedures to estimate conditional probabilities of lake levels for extended horizons (e.g., over a 30-year flood control project, or inter-decadal periods) given current conditions were explored. Nonlinear time series analysis methods using the historical volumes of Devils Lake and selected climate indices as predictors were used to develop the inter-annual forecasts as conditional means of expected future volumes. A variety of time series modeling approaches were explored for the inter-decadal forecasts. Results are presented here for a linear, Bayesian autoregressive time series model that incorporates model and parameter uncertainty. We conclude that direct applications of existing time series analysis methods are not well suited for the development of long-range probabilistic forecasts of Devils Lake. The recent trends of Devils Lake are consistent with large-scale changes seen elsewhere. However, whether these changes are part of the natural long-term variability of climate or represent a changed climate due to human influence remains inconclusive. Consequently, while we are able to relate the recent Devils Lake trend to causative hydroclimatic factors, we are unable to confidently predict the long-term future levels of the lake. Only qualitative remarks are offered to characterize the uncertainty associated with using the past as a guide to the future of Devils Lake.

Trends in Local, Regional and Hemispheric Hydroclimatic Variables

Like most closed basin lakes, the Devils Lake exhibits dramatic fluctuations (Figure 1) over decadal and longer periods, that derive from climatic fluctuations. There is a limited literature (e.g., Lall and Mann, 1995, Mann et al, 1995, Pusc, 1993, Wiche et al., 1986) diagnosing the climatic causes of such fluctuations. The contributing drainage area of such lakes varies with climate state. The chain lakes above Devils Lake and other depressions store water during dry periods, but contribute runoff during protracted wet periods. This change in drainage area may be a key explanation for the dramatic changes in the Lake?s volume, subsequent to moderate changes in the climate signal. Notable increases occur in 1950?s, 1970, 1980?s, and the late 1990?s. These periods are also important at the regional streamflow stations.

It is useful to first look at the annual cycle of monthly changes in the Devils Lake volume (Figure 2), to motivate the search for trends in monthly precipitation, temperature, Sea Level Pressure (SLP), and cloudiness. The influence of ice cover in the winter months decreases the magnitude of changes observed. On average, the lake volume increases in spring (April through June), due to snowmelt runoff. Decreases occur in the summer months, with the greatest decrease usually occurring in August. However, increases or decreases can occur in any calendar month. At first glance, one would suspect that the winter/spring precipitation and summer/fall temperature (and hence atmospheric circulation patterns), are most important for diagnosing the changes in the volume of the lake. Monthly trends and the base climatology of these variables are reviewed next.

Figure 1. (a) Historical, standardized time series of the Great Salt Lake, Utah and Devils Lake, North Dakota. Note the dramatic fluctuations of both N. American closed basin lakes, that are often in phase over the period of common record, while the pattern for the amplitudes of positive or negative excursions is quite different. The “X” shows the approximate DL volume around 1840. Note that the annual DL lake level data prior to 1941 has been stochastically disaggregated to the monthly time-scale. (b) Annual change in volume of the two lakes show much weaker correspondence.

Figure 2. Climatology of monthly lake volume changes. Based on monthly lake volume data from October 1941 to May 1998.

Figure 3. Location of precipitation stations.

The climatology of monthly precipitation amounts for selected stations (Figure 3) is shown in (Figure 4). The data for these stations was obtained from ftp://ftp.ncdc.noaa.gov/pub/data/ushcn/daily/. Note that precipitation peaks in June, and that September/October precipitation can occasionally be as large as the average June precipitation. The range can be dramatic (e.g., 0.5? to 12? for June at Bottineau), even though the total average amounts reflect an arid climate. Winter precipitation is on average much smaller than the summer/fall precipitation in the region. It is consequently remarkable that the largest annual volume increase for the lake is seen in April. The subtle role of subsurface hydrologic processes and evaporation in modulating the lake inflows and hence the volume is indicated.

Monthly precipitation data were used to investigate the variability in precipitation. Data were obtained from the U.S. HCN web site (ftp://ftp.ncdc.noaa.gov/pub/data/ushcn/) and the ?FILNET? version of the data, are used. The accompanying literature notes that the FILNET data has been adjusted for the time of observation bias, maximum and minimum temperature system (MMTS) bias, station moves and changes bias, and contains estimated values for missing/outlier data. These adjustments provide a long, serially complete data set useful. An examination of these records reveals largely consistent precipitation trends in the recent period. Month-by-month trends for the Langdon station are presented in Figure 5. Note that no major positive trends in the winter/spring (Dec-Apr) precipitation are evident, while May through August and October/November exhibit a positive trend in the recent period. Given that the summer and fall are usually periods of lake volume decrease, these precipitation trends are significant in that they may imply a situation where the lake?s annual cycle of decrease may be reversed. The increased regional wetness in the summer may also lead to a higher contributing drainage area and runoff, and to a lower potential evaporation associated with increased local humidity. Recently, Karl et al. (1998) investigated the secular trends in both amount and intensity of precipitation in the U.S. with relatively long data sets (1910-1996). They found positive trends of heavy rainfall in the region covering the nearby upper Mississippi River basin in all seasons, except winter, where a negative trend was found. The largest trend reported for the region was for summer, consistent with the finding of Angel and Huff (1995). These analyses are consistent with our observations from the local station data.

An examination of the 1896 to 1996 monthly temperature record at the Langdon station (Figure 6) shows no unprecedented changes over the recent period of concern. January, April, May and July temperatures have reversed earlier warming trends during the 1990?s. The period of decreasing and extremely low lake levels may be associated with lower summer precipitation and warmer summers. The cooler and wetter summer/fall regime is associated with the recent rise of the Devils Lake.

A comparison between the July and October precipitation trends and the local Sea Level Pressure (SLP) trends is instructive. From Figure 7, we see that for both months the increasing precipitation trend is accompanied by a decrease in the mean monthly SLP for the month. Similar trends in the local SLP are evident for June and August. This observation sets the stage for an examination of trends in the larger-scale precipitation, river flow and atmospheric circulation fields.

Figure 4. Climatology of precipitation at selected stations. The boxplot includes the median (white line), the 25th and 75th percentiles (the box limits), the 10th and 90th percentiles (whiskers), and the outliers (lines outside whiskers). The abbreviations correspond to stations shown in Figure 3.

Figure 5. Raw time series (light solid) and 20-year Loess smooth (heavy solid) of monthly precipitation at the Langdon Experiment Station. Note the May through August and October trends.

Figure 6. Raw time series (light solid) and 20-year Loess smooth (heavy solid) of monthly temperature at the Langdon Experiment Station.

Figure 7. Sea level pressure (SLP) anomaly and Langdon precipitation time series plots for July and October. The heavy line is a 20-year Loess smooth of the raw data (light solid). Note the recent large decrease in SLP in both months and the associated increase in precipitation. The summer SLP values in 1992-98 correspond to a 6-8 m average reduction in the height of the regional 700 mb geopotential surface.

Figure 8. Annual average flow for each water year of the period of record (1874 to 1996) of the Mississippi River at the Clinton, IA gaging station. The raw flows (solid) and two Loess smooths using a 10-year span (dotted) and 60-year span (dashed) are shown.

Baldwin and Lall (1999) note changes in the seasonal and long-term mean flow of the Upper Mississippi River (Figure 8). Specifically, they note changes in the seasonality of flow, precipitation, and atmospheric circulation in the region that translate into a secondary October peak and an enhanced summer peak for the flow at Clinton, IA in the recent period. The general trends in the flow of the Upper Mississippi River are consistent with those of the Devils Lake. Specifically, both the Devils Lake volume and the heaviest smooth of the Upper Mississippi River flows (dashed line in Figure 8) change direction around 1940. There are indications that this change-point is a regional- to continental-scale phenomenon.

An increase in mean annual cloud amounts of 27% occurred at Bismarck, North Dakota between 1930-1950 (Figure 9a). A Bayesian change-point analysis (Lee and Heghinian, 1977) indicates that the most likely year the regime changed is 1940. This coincides with the beginning of a general increase in Devils Lake volume over the next 60 years. One explanation may be that the increase in cloudiness reduces potential evaporation by decreasing incoming solar radiation. Since evaporation is the only way for water to escape a closed-basin lake such as the Devils Lake, such a change could have a dramatic impact. The climatology of cloudiness for the Devils Lake area (Figure 9b) indicates that the months of July and August have the highest proportion of clear days, months that correspond to decreases in the Devils Lake volume on average (Figure 2). Hence, increases in cloudiness in these months would likely decrease evaporative losses from the Devils Lake.

This change in cloudiness is not a local phenomenon. A similar increase occurred in many cities in the western U.S. between 1937-1942 (Baldwin et al, 1999; Lee and Heghinian, 1977). Thus, it appears that the climatic factors that have an impact on Devils Lake are at least regional and approach continental scale.

Figure 9. (a) Time series and 5-year moving average of mean annual cloudiness at Bismark, ND (Steurer and Karl, no date); (b) Climatology of cloudiness at Devils Lake, ND (Jensen, no date).

A continental perspective on the spatial pattern of the 1992-98 precipitation anomalies for July and October is presented in Figure 10, using the U.S. Climate Division data. The July rainfall trend extends over the Upper Mississippi river basin and into the Pacific Northwest, and the extreme Northeastern part of the country. By contrast the southern part of the country has tended to be drier than normal in the 1992-8 period in July. The picture for October reveals that the Devils Lake region is near the core of a significant national pattern of regional positive and negative precipitation anomalies. As a contrast, the precipitation anomaly patterns for a set of dry years (1940-45) for Devils Lake are shown in Figure 11. Note the opposite, but qualitatively similar spatial structure of the anomaly fields, especially for October. Opposite phases of the same climate mechanisms may thus be at play. Similar plots (using the same years) for the winter seasons did not reveal any large, structured precipitation anomalies across the country.

Figure 10. Composites of standardized U.S. precipitation anomalies for 1992-1998 July (a) and October (b). Standard deviations are computed with respect to the 1950-1995 mean at each climate division. The color codes are in units of standard deviation.

Figure 11. Composites of standardized U.S. precipitation anomalies for 1940-1945 July (a) and October (b). Standard deviations are computed with respect to the 1950-1995 mean at each climate division. The color codes are in units of standard deviation.

We now examine the spatial structure of N. Hemisphere summer and fall atmospheric circulation fields through composited anomalies of extratropical SLP for the same years as the precipitation composites presented in Figures 10 and 11. The average summer pressure fields (Figure 12a) are marked by climatological highs in the Pacific and Atlantic Oceans near 35N, and continental low pressure centers over Asia, Southwest U.S., and Eastern Canada. Closed basin lakes such as the Devils Lake and the Great Salt Lake occur in regions where the long-term potential evaporation rate exceeds the precipitation rate. As seen from Figures 12 and 13, such basins correspond to areas with a ridge in atmospheric pressure on average. Persistent changes in large-scale pressure patterns that weaken this ridge bring storm systems to the area. The summer circulation fields are generally weaker than the winter/fall fields (compare Figures 12a and 13a). By October, continental high pressure centers (East Asia, central N. America) become established, and low pressure centers in the northern oceans become more pronounced leading to a wave pattern as the extratropical jet stream gains strength and moves towards the equator. The ?ridge? in the Devils Lake region is pronounced in the average picture reflecting the low probability of large-scale storms that can penetrate the region and lead to precipitation. The 1940-45 October (Figure 13b) is marked by an expanded and stronger central Pacific high pressure system, a more pronounced Aleutian low, and an enhanced continental ridge, and a weaker low East of Canada. These combine to keep storms out of the Devils Lake region. By contrast, the 1992-97 wet period (Figure 13c) is marked by a weakening of the October ridge over the Devils Lake area and an associated broad weakening of the continental and coastal high pressure systems around N. America, and of the low pressure systems East of Canada and in the Aleutians. An intensification and displacement of the East Asian high, and the North Asian low-pressure systems is also noted. These asymmetric changes of the average pressure systems in the regions that control the jet-stream dynamics may allow localized circulations such as the summertime low-level jet to remain established in October, and lead to the spatial structure of the rainfall anomalies (Figure 11) over N. America. Similar changes are evident in the summer analysis in Figure 12.

Figure 12. (a) Mean summer (June, July, August) SLP (mb) for the Northern Hemisphere, north of 20N; (b) The SLP anomaly field for the 1940-45 dry period; and (c) the anomaly field for the 1992-97 wet period. The approximate location of Devils Lake is marked by the *. The Great Salt Lake is located at 42N and 112W.

Figure 13. (a) Mean October SLP (mb) for the Northern Hemisphere, north of 20N; (b) The SLP anomaly field for the 1940-45 dry period; and (c) the anomaly field for the 1992-97 wet period. The approximate location of Devils Lake is marked by the *.

Connections to Low Frequency Climate Indices

An explanation of the recent rise of Devils Lake and a forecast of its future volumes consequently requires the characterization of operative multi-year climate regimes that may affect regional summer and fall precipitation. Three quasi-oscillatory climate regimes have recently been identified as the primary determinants of interannual to interdecadal climate variability in the Northern Hemisphere. These are the El Ni?o Southern Oscillation (ENSO, Battisti and Sarachick, 1995) with interannual (3-7 year) scales of characteristic variability, the Pacific Decadal Oscillation (PDO, Mantua et al., 1997) with interdecadal (16-20-year) characteristics, and the North Atlantic Oscillation (NAO, Kushnir, 1994) with decadal (8-10-years) variability. These three modes are often termed quasi-oscillatory, since they have recurrent phases with preferred time scales rather than an exactly periodic behavior like the seasonal cycle. The ENSO, PDO, and NAO are hypothesized to be the outcome of bi-directional, multi-scale interactions between slowly varying ocean conditions (temperature, height and velocities) and rapidly varying atmospheric conditions (winds and pressure systems). They reflect persistent ocean and atmospheric conditions in the tropical Pacific (ENSO), the extratropical Pacific (PDO) and the Atlantic (NAO) Oceans that can rapidly switch and lead to hemispheric impacts on atmospheric pressure and storm migration patterns. ENSO is by far the best studied and understood of these modes. Interactions between these climate modes and their teleconnections to continental climate have been noted, but are not yet well understood. These are areas of active diagnostic research using data and models. In the current context, the interaction between these modes in producing the atmospheric conditions associated with Devils Lake variations is of interest.

ENSO has been rather active lately, with 1997-8 being one of the strongest El Ni?o events on record, and an extended El Ni?o event persisted from 1991-95. Several “indices” that provide a measure of the ENSO state are available. There is also some discussion (Trenberth and Hoar, 1995, Rajagopalan et al, 1995) as to whether the frequency of ENSO events is changing and whether it is related to anthropogenic climate change. Since this debate is inconclusive, we shall strive to use the indices related to these ?oscillations? as diagnostic tools for understanding oceanic teleconnections to Devils Lake. Here, we have used the Sea Surface Temperature (SST) in the NINO3 region (5N-5S, 150W-90W) in the Eastern tropical Pacific. A positive value of this index corresponds to El Ni?o conditions, and a negative value to La Ni?a conditions. The PDO is represented by the PDO Index develop by Nathan Mantua (Mantua et al., 1997). It corresponds to the leading spatial pattern from a Principal Component Analysis of N. Pacific (30N to 70N) SSTs. An NAO Index is also available, as the difference of normalized sea level pressures (SLP) between Lisbon, Portugal and Stykkisholmur, Iceland. The time series of the NINO3, PDO, and NAO indices have statistically significant cross-correlations at various lags, but have rather different time scales of variation based on spectral analysis.

The time-frequency variation in the three indices, and in the monthly change in the volume of Devils Lake and the Great Salt Lake is examined (Figure 14) through a wavelet analysis (Torrence and Compo, 1998) of their monthly time series. The spectra of these series are generally quite different in their overall character with different frequency bands emphasized. Note the intermittent nature of the narrow band oscillations (indicated by each global wavelet analysis and the corresponding time-frequency plot) in the series, over the period of record. The characteristic time scales of NINO3 are interannual, of the NAO, decadal, and the PDO interannual to interdecadal. The two lakes are more similar to each other than to the indicators, as would be expected. One expects the lakes to have a general long memory behavior. However, they are more organized in frequency bands than say the PDO, which despite preferred interannual and multi- decadal attributes is redder in

Figure 14. Wavelet spectra for NINO3, PDO, NAO, and monthly volume change of Devils Lake, and Great Salt Lake. The full record spectrum is shown to the right. The dashed red line in each such plot is the 95% significance level for red noise. The wavelet spectrum shows the corresponding variation in spectral power for each period as a function of time. For Devils Lake, most of the power at all frequencies is concentrated in the last decade, emphasizing the unusual nature of this period. Considerable time variation in the time-frequency structure as well as common time-frequency structures across the series are also notable.

character. The Devils Lake annual cycle is not nearly as clear as for the Great Salt Lake reflecting somewhat different basin/climate dynamics. The wavelet spectrum for the Great Salt Lake shows a much greater time-frequency commonality with the climate index spectra, than does the Devils Lake. The recent period is clearly the most anomalous for the Devils Lake record.

The role of the three low-frequency climate patterns in generating such an anomaly is explored through an analysis of the correlation between the three climate indices and summer and October continental precipitation in Figure 15. While the spatial patterns of correlation of each index with continental precipitation differ for each season, particularly for the summer, they (a) show strong spatially coherent response structures for each index, and (b) have statistically significant correlations with the general Devils Lake region that are consistent with the increased regional precipitation for these seasons shown in Figures 5 and 7. Note that NINO3 has been positive on average over this period with a protracted positive anomaly over 1991-95 and the large positive anomaly through 1997-8, and is positively correlated with the Devils Lake region precipitation for summer. Likewise PDO has been in its positive phase and is positively correlated while the NAO, which is negatively correlated with precipitation in the region, has been in its negative phase. The correlation patterns of the three indices with the summer 700 mb geopotential height are shown in Figure 16. The correlation of the Devils Lake region’s pressure with NINO3 is not significant. However, the correlations of the PDO and NAO with the regional pressure surface are consistent with the corresponding indications for precipitation. Thus it appears that the conditions in the Pacific and the Atlantic Ocean may jointly influence the atmospheric regime that leads to anomalous summer precipitation in the Devils Lake region. Indeed correlations between the indices and the atmospheric pressure over the U.S. for June through August are consistent with patterns that would steer storms to the Devils Lake area. Similar, but weaker correlations are noted for October.



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