# Presentation: 518373

## ImageAire Lite iiSmall UAV for Geothermal ObservationsGeoff Bland/614.6, Ted Miles/569

## ImageAire Lite ii

- New small UAV with infrared, visible, and narrow band (filtered) _***nadir***_ viewing cameras with real-time video link
- Specifically created for _***geothermal ***_mapping experiments in Yellowstone National Park
- Also suitable for agricultural research and miniature sensor system development
- Includes GPS, temperature, humidity, and pressure measurements, as well as provisions for *Anasphere, inc*. experimental carbon dioxide sensor
- Electric propulsion for quiet, environmentally clean operation
- Hand-launched for minimum operating area requirements
- Suitable for standard shipping, assembles in ten minutes
- Six pound total weight

**Terms:**
_*Nadir*_: A point on the celestial sphere directly below the observer
_*Geothermal*_: relating to or produced by the internal heat of the earth

## Evaluation of Sea Ice in GCM Simulations Claire Parkinson (614.1), Kostya Vinnikov (Univ. MD), Don Cavalieri (614.1)

- Background/Purpose: Global climate models (GCMs) are widely used to make projections about future climate. For these projections to be reliable, it is important that the main climate elements are properly simulated. We have examined how well 11 major GCMs
- simulate polar sea ice (which reflects solar radiation)
- provide insulation between the ocean and atmosphere
- Procedure: From simulation results made available through the efforts of the Intergovernmental Panel on Climate Change (IPCC), we mapped simulated ice distributions and calculated and plotted the simulated annual cycles of Arctic and Antarctic monthly ice extents averaged for 1979-2004, from each of 11 models; we then compared these simulated results with the corresponding observational results derived from satellite passive-microwave data.
- Results: See the next two slides.

- Reference: Parkinson, C. L., K. Y. Vinnikov, D. J. Cavalieri, 2006, Evaluation of the simulation of the annual cycle of Arctic and Antarctic sea ice coverages by 11 major global climate models, Journal of Geophysical Research, 111, doi: 10.1029/2005JC003408, in press.

## A. Sample late summer (yellow) and late winter (blue/yellow) sea ice coverage

- B. Sample annual cycles, observed (dashed) and simulated (solid) for the Northern (red) and Southern (blue) Hemispheres

## Summary of Results

- Model consistencies: All 11 models simulate annual cycles with approximately the correct phasing and with monthly average Northern Hemisphere ice extents to within _+_ 5.1 x 106 km2 of the observed values. They all also properly simulate full wintertime ice cover in Hudson Bay despite its low latitudes and an absence of ice west of Norway (due to the Gulf Stream and North Atlantic Current) despite its high latitudes. None simulates the full effect of the North Atlantic Current.
- Model differences in simulating the annual cycle:
- Excellent Northern Hemisphere models:		--    Models simulate too much ice in Southern Hemisphere:
- -German ECHAM5			         -HadGEM1
- -Canadian CGCM3			         -Canadian CGCM3
- -Japanese MIROC3
- 	   --    Excellent Southern Hemisphere models:		--   Models simulate too little ice in Southern Hemisphere
- -Australian CSIRO Mk3UK			         -Japanese MIROC3
- 					         -Norwegian BCCR BCM2
- 					         -US GISS ER
- 					         -US GFDL CM2.1
- 					         -French IPSL CM4

*Models used/acronyms: *
- 1. United Kingdom (UK) Hadley Centre Coupled Model 3 (HadCM3)
- 2. UK Hadley Centre Global Environmental Model 1 (HadGEM1)
- 3. German European Centre Hamburg Model 5 (ECHAM5)
- 4. Canadian Coupled Global Climate Model 3 (CGCM3)
- 5. Australian Commonwealth Scientific and Industrial Research Organization Mark 3 model (CSIRO Mk3)
- 6. Japan Model for Interdisciplinary Research on Climate version 3.2 (MIROC3)
- 7. Norwegian Bjerknes Centre for Climate Research Bergen Climate Model version 2 (BCCR BCM2)
- 8. US Goddard Institute for Space Studies ER model (GISS ER)
- 9. French Institut Pierre Simon Laplace coupled model 4 (IPSL CM4)
- 10. Russian Institute of Numerical Mathematics CM3.0 model (INM CM3)
- 11. US Geophysical Fluid Dynamics Laboratory coupled model 2.1 (GFDL CM2.1).

## Evaporation Minus Precipitation

**from Salinity Changes**
**David Adamec & Zhen Li, NASA/GSFC**

**Study:**
- A 1/2 degree, 41-year (1964-2004) ocean model simulation of the Pacific Ocean is used to investigate the possibility of using temporal variations in the ocean salinity to determine the surface buoyancy forcing induced by precipitation and evaporation.
**Procedure:**
- Two correlation maps are presented.  All correlations are calculated using monthly averages and one-degree horizontal resolution, the nominal resolution of planned Aquarius salinity returns.
**Results (associated with images on next slide):**
- The largest magnitude correlations occur in the subtropics (blue contour areas) , but here the two fields are anti-correlated, which indicates an active dynamical process strongly induced by the surface forcing is forcing salinity changes, and not the surface forcing itself.
- The largest positive correlations occur off the west coast of continents, and have a maximum of about 0.4. Values greater than 0.13 are significant.  However, these correlations indicate that only 10%-16% of the salinity variability is directly correlated with the surface forcing in these areas. Most variability is attributable to other mechanisms.
**Future Science Goals and the Aquarius Mission:**
- One of the science goals of the Aquarius mission is to estimate surface salinity forcing from observed salinity changes.  These results suggest reliable estimates of that forcing will have to be obtained through a multi-step process.  Clearly knowledge of horizontal _***advective***_ effects, which could be estimated through a combination of Aquarius and _***altimeter ***_measurements, will be crucial.   For areas near the equator, it is apparent vertical mixing and transport processes are at work, and accurate models of these processes will be necessary accurate estimates of evaporation minus precipitation forcing.

**Terms:**
_*advective (advection*_): Horizontal transport of air or atmospheric properties, such as wind
_*altimeter*_: An instrument that indicates the altitude of an object above a fixed level

## Correlation between monthly salinity changes and surface forcing due to the difference between evaporation and precipitation.

- Correlation between monthly salinity changes and salinity changes due to horizontal advection.

**Negative values indicate a direct correlation**

- *Note over most of the area, salinity changes are directly related (blue areas) to the horizontal advection, and those direct correlations exceed 0.5 in many areas. Thus, in this simulation it is critical to have knowledge of advective effects if one is to determine the main causes for changes
- in salinity.

## ComRAD Tree Experiment  –  Summer, 2006

***ComRAD Tree Experiment  –  Summer, 2006***

- Peggy O’Neill
- NASA GSFC

## 

OBJECTIVE:  To improve the accuracy of soil moisture retrieval through different types of tree stands

APPROACH:   To acquire data over at least one soil moisture drydown cycle each during peak biomass, fall 	         leaf drop, and spring green up and to examine synergism between laser, microwave, and 	         visible/infrared data for improved soil moisture retrieval

EXPECTED RESULTS:
optimize soil moisture retrieval algorithm for small/medium trees
address major gap in knowledge base of microwave soil moisture retrievals
eventually extend accurate soil moisture retrievals from global microwave space missions to more areas of the Earth’s surface than is currently feasible

***OBJECTIVE*****:**  To *improve the accuracy of soil moisture retrieval through different types of tree stands*
***APPROACH:***   To acquire data over at least one soil moisture drydown cycle each during peak biomass, fall 	         leaf drop, and spring green up and to examine synergism between laser, microwave, and 	         visible/infrared data for improved soil moisture retrieval
***EXPECTED RESULTS:***
- optimize soil moisture retrieval algorithm for small/medium trees
- address major gap in knowledge base of microwave soil moisture retrievals
- eventually extend accurate soil moisture retrievals from global microwave space missions to more areas of the Earth’s surface than is currently feasible

***ComRAD Tree Experiment  –  Summer, 2006***