# Presentation: 859400

## Climate & Marine Ecosystems(or Axel Ljungman, where are you when we need you?)

**Climate & Marine Ecosystems****(or Axel Ljungman, where are you when we need you?)**

**Woody Turner**
**NASA Science Mission Directorate**
**NOAA-NASA Workshop on Integrating Satellite **
**  ****Data into Ecosystem-based Management of **
**  ****Living Marine Resources**
**Monterey Bay Aquarium Research Institute**
**Moss Landing, CA**
**May 3, 2006**

## Climate, Fish, and Time

- Figure 6.2 Cyclic temperature fluctuations and Japanese sardine outbursts for last 400 years by Japanese historic chronicles 1640-1880 and for 1920-1998 from fisheries statistics.
- (From: FAO. 2001. Climate change and long-term fluctuations of commercial catches: the possibility of forecasting, by L.B. Klyashtorin. FAO Fisheries Technical Paper No. 410. Rome. 86 pp.  Thanks to Gary Sharp for the reference)

**!**

**Climate, Fish, and Time**

**Notes:**
Another single index of climate change is the Atmospheric Circulation Index (ACI) that characterizes the periods of relative dominance of either "zonal" or "meridional" transport of the air masses on the hemispheric scale. ACI, also known as the Vangengeim-Girs Index, has been calculated from data on basic atmospheric activities in the Atlantic-Euroasian region for more than 100 years (Girs 1974).
George Vangengeim, the founder of ACI, is a well-known Russian climatologist. The Vangengeim-Girs classification is the basis of the modern Russian climatological school of thought. According to this system, all observable variation in atmospheric circulation is classified into three basic types by direction of the air mass transfer: Meridional (C); Western (W), and Eastern (E). Each of the above-mentioned forms is calculated from the daily atmospheric pressure charts over northern Atlantic-Eurasian region. General direction of the transfer of cyclonic and anticylonic air masses is known to depend on the distribution of atmospheric pressure over the Atlantic-Eurasian region (the atmosphere topography).
The recurrence of each circulation form (W, E, or C) during the year is expressed in days. Total annual duration of the three circulation forms sums therefore to 365 (or 366 in a leap year). The index is defined by the number of days with the dominant form of atmospheric circulation. It is more conveniently expressed as an anomaly (actual data minus the long-term average). The sum of anomalies can be displayed in a chart of the so-called integral curve of the atmospheric circulation. The annual sum of the occurrence of all circulation anomalies is equal to zero: (C) +(W) + (E) = 0.
The periods dominated by any single form of atmospheric circulation have alternated with a roughly 30-year period for the last 100 years. These periods were named "Circulation epochs". These may be pooled into two principal groups: meridional (C) and combined "latitudinal" epochs (W + E): (W + E) = - (C)
Meridional (C) circulation dominated in 1890-1920 and 1950-1980. The combined, "zonal" (W+E) circulation epochs dominated in 1920-1950 and 1980-1990. Current "latitudinal"(WE) epoch of 1970-1990s is not completed yet, but it is coming into its final stage, and so the "meridional" epoch (C-circulation) is now in its initial stage. (It will be useful for the reader to note here the relation that shows that the "transition" from C to W-E is continuous, and the equation balances to 100%, in the form of a simple graphic without any other variables included).
It was found that "zonal" epochs correspond to the periods of global warming and the meridional ones correspond to the periods of global cooling. (Lamb 1972; Lambeck 1980). The generalised time series on the atmospheric circulation forms for 1891-1999 were kindly placed at our disposal by the Federal Arctic and Antarctic Research Institute (AARI) in St. Petersburg (Russia). This is also consistent with the theories and observations described by Leroux (1998).
The third important index is Length of Day (LOD) - a geophysical index that characterizes variation in the earth rotational velocity. Full time series of LOD cover more than 350 years, with the most reliable data obtained in the last 150 years (Stephenson and Morrison 1995). The long-term LOD dynamics is in close correlation with the dynamics of the main commercial fish stocks (Klyashtorin and Sidorenkov 1996).
LOD Index is calculated as a difference of two values: actual (astronomical) length of day and the standard one. The continual registration of LOD and publication of the corresponding data is carried out by the International Time Bureau in Paris. The correlation coefficients were calculated using non-smoothed data ranges. Smoothed values, obtained using a Gaussian filter, were only used to improve the curve plots in the figures. Statgraphics (1988) software was used to detrend the time series.
Let us compare the dynamics global geophysical (LOD) and climatic indices for the last 150 years. For a more convenient visual comparison of the dT, ACI and the earth rotation velocity dynamics, the negative (-LOD) instead of the directly calculated LOD values was used. The dynamics of -LOD, dT and ACI for the last 140 years can be viewed as multi-decadal fluctuations against the background of age-long trends (Figure 2.1).
Global dT is known to have an ascending linear trend 1861-1999 with the increment of 0.059o per 10 years (Sonechkin 1998).

The first type, zonal circulation, is characterised by increasing intensity of the zonal circulation at all latitudes and pole-ward shift of the wind intensity maximums. The circulation is accompanied somewhat by a decrease in the overall range of surface-air temperature between the equator and poles and by an overall increase in the mean global surface-air temperatures. Ocean-surface temperatures tend to increase in high latitudes. The second type, meridional circulation, is characterised by weakening in zonal circulation, shift of the main atmospheric streams toward lower latitudes, and overall decrease in global temperature (Lamb 1972). Both easterly and westerly winds increase during the zonal type of circulation and both decrease in the periods of the meridional type of the circulation (Lambeck 1980).
Atmosphere is the most variable component of the global geophysical system exchanging relatively large proportions of its angular momentum with the solid earth below compared with other components (Salstein et al. 1993). A number of publications suggest that seasonal, inter-seasonal and inter-annual variations of the earth's rotation velocity are directly proportional to the relative angular momentum at the atmosphere, which primarily depends on the velocity of zonal winds (Langley et al. 1981; Rosen and Salstein 1983; Robertson 1991). On longer time scales, changes in LOD are correlated with the El Niño Southern associated with ENSO events (Salstein and Rosen 1986; Dickey et al. 1992a, b)

## ACI “Zonal” Species

- (From: FAO. 2001. Climate change and long-term fluctuations of commercial catches: the possibility of forecasting, by L.B. Klyashtorin. FAO Fisheries Technical Paper No. 410. Rome. 86 pp.)

**ACI “Zonal” Species**

**Notes:**
The total world marine catch averages 73 million tons per year, which consists of about 550 species. However, as few as 12 species (about 2 percent of total number of commercial species) provide up to 40-50% of the world marine catch. These are: Japanese, Californian Peruvian and European sardine, Pacific salmon, Alaska pollock, Chilean jack mackerel, Peruvian anchovy, Atlantic cod, Atlantic and Pacific herring, South African sardine, and Peruvian anchovy. Seventy to ninety percent of the total commercial catches in Russia, Norway, Iceland, Japan, Peru, and Chile depend on these commercial species (hereafter referred to as the "major commercial species"). The remaining 538 species constitute the second half of total marine catch.
In the late 1980s, the annual commercial catch in the Pacific was close to 54 million tons. The total catch of the 5 main Pacific commercial species (Japanese and Peruvian sardine, Alaska pollock, Chilean jack mackerel, and Peruvian anchovy) made up 52% of this total (28 million tons). In the late 1960s, the total marine catch in the Atlantic was estimated to be 22 million tons, of which three of the five main Atlantic commercial species (Atlantic cod, Atlantic herring and South African sardine) made up 44% (9.6 million tons).

It was found that the 12 major commercial species could be divided into two principal groups, based on the peculiarities of their long-term dynamics (Fig. 3.1 and 3.2). The first group consisted of Japanese, Californian and Peruvian sardine, Pacific salmon, Alaskan pollock, Chilean jack mackerel, Peruvian anchovy and European sardine. The maximum catch of these species occurred in the late 1930s and early 1990s, with the minimum catch in the 1960s. The second group comprises Atlantic cod, Atlantic and Pacific herring, South African sardine, and Peruvian anchovy. The maximum catch for this group was in the 1960s, and the minima occurred in the 1930s and 1990s. Therefore, the catch dynamics of the two groups are opposite in phase.

The Atmospheric Circulation Index (ACI), though measured in the Atlantic-Eurasian sector of the Western Hemisphere, almost coincides in dynamics with both dT and LOD, and therefore can be considered as a global index. It has no overall trend and can be directly compared with fishery indices, such as catch dynamics.
The ACI dynamics (Figure 3.6A) exhibits regular, roughly 30-year, alternation of the "circulation epochs" characterised by predominance of either «zonal» or «meridional» components. These epochs correspond to the periods of either global warming or global cooling. In fact, only the upper parts (peaks) of C ("meridional") and WE ("zonal") curves are of practical significance (Figure 3.6B)
The alternation of "cool" (meridional) and "warm" (zonal) circulation epochs correspond also to the long-term changes in the catch of main commercial species over the Atlantic and Pacific. The maximum production of seven of the twelve major commercial species fall on the "warm" period, whereas the production of the remaining 5 species is in good agreement with the "cool" periods (Klyashtorin 1998).
The relationship between the circulation epochs and maximums of the major commercial fish production in both the Atlantic and Pacific oceans is shown in Figure 3.7. The long-term fish production follows the regular alternation between the "meridional" and "zonal" circulation epochs. Regular alternation of the epochs for the last 110 years suggests that the present epoch of "zonal" circulation is coming into its final phase and the new "meridional" epoch is due.
What changes are expected to occur in commercial fish production in the oncoming circulation epochs? The most important fishery regions are the North Atlantic and North Pacific. A close agreement between the dynamics of climatic indices and commercial catches in the North Pacific is shown in Figure 3.8. The long-term fluctuations of the major pelagic fish species in the North Pacific can be described as the sequential passing of two climate-governed "waves" with the maxima falling on 1940-50s and 1970-90s.
The last (current) "wave" is coming into its final phase similar to that of 40-50s. This means that the population of main commercial species in North Pacific (Japanese Sardine, Alaska pollock and Pacific salmon) is expected to decline in the near future.
Unlike the North Pacific, the maximum of commercial fish production in the North Atlantic falls on the "meridional" ACI epoch of 1950-70s. The oncoming "meridional" circulation epoch in the North Atlantic is similar to that of 50-70s when production of herring, and cod and other gadoids reached up to 9 million tons. Therefore, with the coming of the meridional circulation epoch, we may expect an increase in these species population (herring, and cod and other gadoids) in the North Atlantic over the next decade.
More recent data confirm this viewpoint. The population of Atlantic (and Pacific) herring has already started to rise. Since the cod population has always followed herring in the past, the same dynamics may be expected in the future decade as well.

## And

- (From: FAO. 2001. Climate change and long-term fluctuations of commercial catches: the possibility of forecasting, by L.B. Klyashtorin. FAO Fisheries Technical Paper No. 410. Rome. 86 pp.)

**And**
**ACI Meridional Species**

## After Removing the LT Trend of Increasing Catch 

- (From: FAO. 2001. Climate change and long-term fluctuations of commercial catches: the possibility of forecasting, by L.B. Klyashtorin. FAO Fisheries Technical Paper No. 410. Rome. 86 pp.)

**After Removing the LT Trend of Increasing Catch **

**Notes:**
The production of main commercial fish species in both the Atlantic and Pacific undergoes long-term climate oscillations (Figure 3.5A). The corresponding curves reflect two principal processes: (i) large-scale regular fluctuations in the fish production, and (ii) gradual increases in fishing grounds and fishing capacity. The capacity of the world fishing fleets has increased almost linearly by 12 million tons over 1973-1993. However, the relative commercial catch per 100 ton capacity has stayed practically stable for the same period (FAO 1994; Moiseev 1995). When the long-term trend is removed, the regular fluctuations of the major commercial fish production in both Atlantic and Pacific oceans become clearer (Figure 3.5B).

## A Global ACI & Regional Indices

**A Global ACI & Regional Indices**

**Notes:**
In relation to the dynamics of regional climatic indices, there are two basic questions that need to be addressed. (1) How closely are long-term changes in regional indices correlated with changes in global indices? (2) Do the dynamics of regional indices correspond to changes in the stocks, and therefore catches, of the main commercial species? Figure 4.1 compares the dynamics of global and regional climatic indices with commercial catches of major fish species. It is readily seen that the dynamics of regional North Pacific climatic indices (ALPI, NPI, PDO) and the global geophysical index (- LOD) are very similar both in phase and magnitude (Klyashtorin 1999).
The Southern Oscillation Index (SOI) also corresponds closely in phase (but not in magnitude) to the dynamics of regional North Pacific climatic indices and (-LOD).
All four regional climatic indices (ALPI, NPI, PDO, and SOI) and LOD have two main (1930s and 1980s) and one intermediate (1960s) maxima. At the same time, global indices dT and ACI have only two maxima of 1930s and 1990s, with an interim period of about 60 years.
Interestingly, the dynamics of the PDO index (the sea surface temperature) has a weak intermediate maximum during the 1960s, but otherwise the general run of PDO is similar to the dT and ACI dynamics.

## El Viejo and La Vieja

**(Chavez et al. 2003 *****Science *****299:217-221.  From Anchovies to Sardines and Back: Multidecadal Change in the Pacific Ocean)**

**El Viejo and La Vieja**

**Notes:**
Barber: “a basin-wide ecosystem”

In the Pacific Ocean, air and ocean temperatures, atmospheric carbon dioxide, landings of anchovies and sardines, and the productivity of coastal and open ocean ecosystems have varied over periods of about 50 years. In the mid-1970s, the Pacific changed from a cool "anchovy regime" to a warm "sardine regime." A shift back to an anchovy regime occurred in the middle to late 1990s. These large-scale, naturally occurring variations must be taken into account when considering human-induced climate change and the management of ocean living resources. 

## Pattern

**Pattern**

**Process**

**Seeing Patterns but How Do We Get to Processes?**

**“****Based on these correlations, the case is persuasive that the changes in atmospheric circulation led to changes in ocean circulation, which in turn led to changes in the productivity of fish stocks. ... **
**However, one feature of the correlations is that there are often time lags of several years between the physical changes and the shifts in population biomass.  For each stock, it will be necessary to trace the changes in food webs induced by the physical environment, and test the plausibility of the observed time lags.”**
**(Mann, K.H. & J.R.N. Lazier. 2006, *****Dynamics of Marine Ecosystems *****p.439)**

**Notes:**
Pattern=nonrandom, repetitive organization
Process=cause of pattern

## Models Definitely Help

**(Source: UME/Fei Chai)**

**Models Definitely Help**

## Need: Observations of Organisms & Subsurface 

**(From JPL OurOcean Portal http://ourocean.jpl.nasa.gov/cgi-bin/topp_plot.cgi)**

**Need: Observations of Organisms & Subsurface **
**Answer: TOPP! **

## Another Workshop?!

**Another Workshop?!**

** ****Pacific Grove, CA 16-18 July 1996**
** ****NOAA, NASA, Navy, NSF, Canada, UK, Academia**
** ****Focus on Information Sharing (We will do this too.)**
** ****Also, a high priority recommendation for a “Demonstration of the benefits of applied environmental data in fisheries” associated with a call for projects**
** ****This is where this workshop picks up the ball from 1996 & attempts to run with it!**
** ****After exchanging information about our observations, models, & data systems, we will use the break-out groups to talk about projects **
** ****Goal: 1-4 draft reports describing specific, detailed project concepts for NOAA/NASA to consider and potentially begin in FY06-07**** ****  **

## One Way Forward

**Earth System Models**

**Societal Benefits**
**Identify specific areas that the tools address**

**Predictions/Forecasts**

**Observations, Parameters & Products**

**Earth Observations **

**Decision ****Support Tools**

**One Way Forward**

**Break-out Topics:**
**Potential for Improving Stock Assessments & Fisheries Models with Satellite Data**
**Satellite & Model Data Usage & Habitat Classification**
**What are the requirements for observations & models for decision support?**
**CAUTION: Please Don’t Forget *****In Situ***** Data!**

## Workshop Goals & Products

**Goals:**
**Document potential for current & proposed satellite observations & related Earth system models to support NOAA ecosystem-based management**
**Identify models & assessments in use by NOAA Fisheries that could be improved by satellite data & related models**
**Identify requirements & gaps & develop strategies to facilitate the utilization of satellite data & related models for NOAA Fisheries**
**Products:**
**Evaluation report for NOAA & NASA identifying potential projects**
**Articles on workshop results**

**Notes:**
Evaluation Report is for NOAA’s and NASA’s consideration of potential projects 
Article(s) should discuss requirements and gaps.

## Backup Charts

## Slide 15

**Notes:**
NASA Seasonal-to-Interannual Prediction Project (NSIPP) coupled model forecast; modeled initialized with atmosphere/ocean conditions on 2/04 did 12-month assimilations (predictions); typically they run 8 runs, each perturbing conditions a bit; an ensemble forecast; then chose the forecast that is closest to the mean of the 8; SST most important initial condition; ocean has slowest response to varied forcing & drives the system; SST data assimilated from buoys, GRACE (bottom pressure), etc. for a fully-coupled 3D ocean model (includes depth); soil moisture varies next fastest & fastest varying is the atmospheric winds; basis for this is Globel Modeling & Assimilation Office (GMAO) ocean model; with winds from National Centers for Environmental Prediction (NCEP) atmosphere model (with some scatterometry data used to initialize it); oceans give one the 6-month to 1-year level information vis-à-vis predictability; land surface gives one about a 1-month window of predictability; atmosphere=predictability of probability distribution of about 2 days to 1 week    

- %

**(chart courtesy of NASA/Tsengdar Lee)**

**Notes:**
Southern hemisphere accuracies catching up to those for the northern hemisphere due to satellites; 500 millibar; European Centre for Medium-Range Weather Forecasts is very aggressive about incorporating new satellite observation into algorithms; when Atmospheric Infrared Sounder (AIRS) data incorporated, 5-day forecast accuracy improved by 6 hours (= 1 decade’s worth of improvement) 

## Slide 17

**Notes:**
~1 to 2 terabytes/day from satellites; >4 petabytes (>4 x 1024 terabytes) data overall

## Slide 18

**Notes:**
ESMF is a tool for allowing different GCMs with different grids and time steps to talk to one another by regridding inputs from one to match another, e.g., allowing an NCAR atmosphere model, a GFDL ocean model, and NSIPP land model using discrete grids and time stepping to regrid to a common framework.  The “cost” is to the those who have to ensure that the appropriate “metadata” are coded with the model. 

## Data Rich But Analysis Poor?

**Data Rich But Analysis Poor?**

**Notes:**
“data-rich but analysis-poor” from Clery & Voss Science 308:809

20 SGI® AltixTM 3700 superclusters, each with 512 processorsGlobal shared memory across 512 processors 
20 terabytes total memory
Storage Online: 440 terabytes of Fibre Channel RAID storageArchive storage capacity: 10 petabytes 

## GEO: A Global Framework?

**GEO: A Global Framework?**

**Notes:**
Group on Earth Observations

## A Plethora of Biodiversity Data

**Global Mammal Assessment **

**Forest Inventory and Analysis National Program**

**A Plethora of Biodiversity Data**

**GenBank **

**The Species Analyst**

**IUCN Red List**

**World Database on Protected Areas**** **

## Applications of National Priority

**Applications of National Priority**

**Public Health**

## NASA Applied Sciences Paradigm 

**Data**

**Predictions**

***High Performance Computing***
***Communication***
***Visualization***
***Standards & Interoperability***

**Decision**
**Support**_** Tools**_

**Earth **_**Observations**_

**Satellite**
***Airborne***
***in situ***

**Earth Science**_** Models**_

**Oceans**
**Land**
**Atmosphere**
**Ecosystem**

***Inputs******	******            Outputs         Outcomes             Impacts***

**Assessments**
**  ****-  Decision****     Support**
**     ****Systems**
**  ****-  Scenarios**
**     ****Analysis**

***Value & benefits to citizens and society***

**Observations**

- NASA & Research Partners

- Applications Partners

- Partners’ Interface

**Policy **
**Decisions**
**Management**
**Decisions**

**NASA Applied Sciences Paradigm **
**(& Also GEO)**

## Ecological Forecasting at NASA

**Species Distributions**
**Ecosystem Fluxes**
**Ecosystem Productivity**
**Population Ecology**
**Land Cover Change**

_*Ecological Niche*_ (GARP)
_*Scalable spatio-temporal models*_ a la CSU’s NREL
_*Regional Ocean Models & Empirical Atmospheric Models coupled with ecosystem trophic models*_
_*Ecosystem*_ (ED, CASA)
_*Population & Habitat Viability Assessment*_ (VORTEX, RAMAS GIS)
_*Biogeography*_ (MAPSS, BIOME3, DOLY)
_*Biogeochemistry*_ (BIOME-BGC, CENTURY, TEM)

**SERVIR (Spanish acronym for Regional Visualization & Monitoring System) for sustainable environmental management**

_*Land cover*_: MODIS, AVHRR, Landsat, ASTER, ALI, Hyperion, IKONOS/QuickBird
_*Topography/Vegetation Structure*_: SRTM, ASTER, IKONOS, LVIS, GLAS, Radars
_*Primary Productivity/Phenology*_: AVHRR, SeaWiFS, MODIS, Landsat, ASTER, ALI, Hyperion, IKONOS, QuickBird, AVIRIS
_*Atmosphere/Climate*_: AIRS/AMSU, TRMM (PR, LIS, TMI), AVHRR, MODIS, MISR, CERES, QuikScat, AMSR-E, CloudSAT
_*Ocean*_: AVHRR, SeaWiFS, MODIS, TOPEX/Poseidon, JASON, AQUARIUS
_*Soils*_: AMSR-E, AIRSAR

**MesoStor Data System**
**Online Mapping**
**Decision Support**
**Visualization Tools  **

**First-ever effort to manage a global hotspot of biodiversity, i.e. Mesoamerica, at a regional scale through the coordination of the activities of 7 countries – a model for other regions**
**Predict the impacts of changing land-use patterns & climate on the ecosystem services that support all human enterprises **
**Develop ecological forecasts with reliable assessments of error**

**Ecological Forecasting at NASA**

**Land Cover/Land Use & Disturbances (e.g., fire)**
**Species Composition**
**Biomass/Productivity**
**Phenology**
**Vegetation Structure**
**Elevation**
**Surface Temperature**
**SST, SSH, Circulation,  Salinity, & Sea Ice**
**Atmospheric Temp.**
**Soil Moisture**
**Precipitation**
**Winds**

**Marine Fisheries Forecasting**

**Combine physical ocean & ecosystem trophic-level models to predict how climatological changes driven by ENSO & PDO events will affect regional fisheries**

**Protected Area Management**

**Terrestrial Observation & Prediction System (TOPS)**
**NatureServe Vista**
**Fire Information for Resource Management **
**Albertine Rift DSS**

*(Future Mission)*

- April 4, 2006

_**DECISION SUPPORT TOOLS**_

- Observations

_**VALUE & BENEFITS**_

_**EARTH SYSTEM MODELS**_

_**EARTH OBSERVATORIES**_

- Predictions

**If-Then Scenarios for Ecosystem Responses To Change**

## NASA satellites track episodic equatorial Pacific blooms that can be of similar

- size as the rich Peruvian coastal upwelling system
- and therefore must have important ecological consequences

- The blooms follow El Niño events and do not seem associated with local processes

- Ryan et al., in press, JGR-Biosciences; slide courtesy of MBARI/F. Chavez

## Before El Niño

- During El Niño

- NASA models suggest that the blooms may be associated with an El Niño intensification of the
- New Guinea Coastal Undercurrent (NGCUC) that scrapes the continental shelf and increases
- the transport of iron into the equatorial Pacific via the Equatorial Undercurrent (EUC)

- ROMS model output

- (slide courtesy of MBARI/F. Chavez)

## Ecological hindcasting of biogeographic responses to climate change in intertidal ecosystemsBrian Helmuth, David Wethey, Venkat Lakshmi and Jerry HilbishUniversity of South Carolina, Columbia

- Inputs from multiple R/S platforms are used to generate hindcasts of body temperatures of key coastal invertebrate species
- Body temperatures, coupled with data on physiological tolerances, are used to forecast and hindcast shifts in species ranges
- Here, sea surface temperatures (AVHRR 36km) from February 1984 and 1998 show that the 10°C winter isotherm moved from northern Spain to Brittany.  The left arrow is the southern limit of barnacle species* *in 1985, the right arrow was our prediction for 2003 in our grant proposal.
-  2005 Field surveys conducted by our group indicate our prediction was correct.

**February 1984**

**February 1998**

**Notes:**
The primary goal of this project is to connect remote sensing (R/S) data (TOVS, AIRS, AVHRR, MODIS, ASTER) to ground-level models of heat exchange at the scale of intertidal organisms.  We used these predictions to explore the range boundaries of key species of intertidal invertebrates, and then mechanistically to explore the likely ecological and biogeographic effects of climate change on intertidal ecosystems.  This project details an important and novel method for applying remote sensing data to marine physiological ecology, and will provide critical information on spatial and temporal variability in organismal temperatures that is otherwise unavailable.  Our key accomplishments have been the development of a series of mechanistic models that allow us to predict the body temperature of intertidal invertebrates using remote sensing data as inputs.  Comparisons of our simulations against ground-based data collected using a series of biomimetic sensors designed to mimic the thermal characteristics of intertidal mussels and barnacles confirms that we can, on average, predict average daily maximum body temperatures to within 2-2.5°C.  Importantly, this level of error is within our ability to measure the temperatures of these animals in the field (where daily temperatures often fluctuate by 30°C or more in a matter of hours).
We have also tested the ability of our models to predict species range boundaries based on direct physiological limitation of organisms, and have found that in many cases we are able to accurately predict current range boundaries, as well as past shifts in species range boundaries.  We have also begun to explore the influence of indirect effects of climate on species interactions through its effects on rates of predation and competition. 
Finally, we have used our models to explore the sensitivity of intertidal organisms to future changes in climate, and have shown the sensitivity of mussel body temperature varies with latitude. 

## And Then Where To?

*Helicobacterium pylorii* Genome from:
- http://biocrs.biomed.brown.edu/Books/Chapters/Ch%2038/Pylori-Genome.gif

- (from USGS Global Visualization Viewer)

**A Grand Synthesis for the 21****st**** Century**

## Molecular Data & Hindcasting Life

**Molecular Data & Hindcasting Life**

## Remote Sensing of the Sabbath 

**GOME Data from U of Heidelberg  Bierle Atmos. Chem. Phys. Discuss, 2003**

- Day of Week analysis helps to separate anthropogenic source types.
- Five Years from GOME (or
- 1 week of Geostationary)

- 5 yr weekly average data from GOME

**Remote Sensing of the Sabbath **
**(or Remote Sensing of Anthropogenic Sources)**

**Notes:**
Each point a 5-year average; total column nitrogen dioxide (NO2) from GOME on ERS-2; NO2 is a major pollutant; mosaic may be Dobson units;
Need to link geophysical models to econometric models.